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1.  Development of the Diabetes Indicators and Data Sources Internet Tool (DIDIT) 
Preventing Chronic Disease  2005;3(1):A20.
Developing a Web-based tool that involves the input, buy-in, and collaboration of multiple stakeholders and contractors is a complex process. Several elements facilitated the development of the Web-based Diabetes Indicators and Data Sources Internet Tool (DIDIT). The DIDIT is designed to enhance the ability of staff within the state-based Diabetes Prevention and Control Programs (DPCPs) and the Centers for Disease Control and Prevention (CDC) to perform diabetes surveillance. It contains information on 38 diabetes indicators (measures of health or factors associated with health) and 12 national- and state-level data sources. Developing the DIDIT required one contractor to conduct research on content for diabetes indicators and data sources and another contractor to develop the Web-based application to house and manage the information. During 3 years, a work group composed of representatives from the DPCPs and the Division of Diabetes Translation (DDT) at the CDC guided the development process by 1) gathering information on and communicating the needs of users and their vision for the DIDIT, 2) reviewing and approving content, and 3) providing input into the design and system functions. Strong leadership and vision of the project lead, clear communication and collaboration among all team members, and a commitment from the management of the DDT were essential elements in developing and implementing the DIDIT. Expertise in diabetes surveillance and software development, enthusiasm, and dedication were also instrumental in developing the DIDIT.
PMCID: PMC1500969  PMID: 16356373
2.  Update from CDC’s Public Health Surveillance & Informatics Program Office (PHSIPO) 
Objective
To provide updates on current activities and future directions for the National Notifiable Diseases Surveillance System (NNDSS), BioSense 2.0, and the Behavioral Risk Factor Surveillance System (BRFSS) and on the role of PHSIPO as the “home” at CDC for addressing cross-cutting issues in surveillance and informatics practice.
Introduction
The practice of public health surveillance is evolving as electronic health records (EHRs) and automated laboratory information systems are increasing adopted, as new approaches for health information exchange are employed, and as new health information standards affect the entire cascade of surveillance information flow. These trends have been accelerated by the Federal program to promote the Meaningful Use of electronic health records, which includes explicit population health objectives. The growing use of Internet “cloud” technology provides new opportunities for improving information sharing and for reducing surveillance costs. Potential benefits include not only faster and more complete surveillance but also new opportunities for providing population health information back to clinicians.
For public health surveys, new Internet-based sampling and survey methods hold the promise of complementing existing telephone-based surveys, which have been plagued by declining response rates despite the addition of cell-phone sampling. While new technologies hold promise for improving surveillance practice, there are multiple challenges, including constraints on public health budgets and the workforce. This panel will explore how PHSIPO is addressing these opportunities and challenges.
Methods
Panelists will provide updates on 1) PHSIPO’s role in engaging health departments, the organizations that represent them, and CDC programs in shaping national policies for implementing the Meaningful Use program, 2) how the BioSense 2.0 program is supporting growth in syndromic surveillance capacity, including its partnership with ISDS in developing standards for syndromic surveillance as part of Meaningful Use, 3) improvements that are underway in strengthening the NNDSS, including efforts to improve CDC’s support for health department disease reporting systems and to develop a “shared services” approach that could provide a platform for streamlining the exchange of information between health departments and CDC, 4) pilot development of Internet-based panels of survey volunteers to supplement existing telephone-based sampling in the BRFSS and of approaches to extend BRFSS survey information through consent-based linkage of survey responses to selected measures recorded in respondents’ EHRs.
Results
Potential questions or discussion points that might arise include: What can or should be done to assure that the population health objectives of Meaningful Use are fulfilled? What are the lessons learned to date in leveraging investments in the Meaningful Use of EHRs to improve disease reporting and syndromic surveillance systems? What are the next steps in developing BioSense 2.0 to assure that it leads to strengthened surveillance capacity at both state/local and regional/national levels? How can insights from the BioSense redesign be applied to improve case reporting and other surveillance capacities? What is CDC doing to address states’ concerns about the growing number of CDC surveillance systems? How will national discussions about the future of public health affect the future surveillance practice? What can be done to assure the ongoing representativeness of population health surveys? Is it feasible to link BRFSS responses to information obtained from EHRs? How can data from surveillance become part of the real-time evidence base for clinical decision making?
Conclusions
The intended outcome of the panel is to foster a conversation between the panelists and the audience, to inform the audience about recent developments in PHSIPO, to obtain insights from the audience about innovations and ideas arising from their experience, and to generate new ideas for approaches to meeting the needs of public health for surveillance information.
PMCID: PMC3692948
Surveillance; BioSense 2.0; Notifiable Diseases; BRFSS—Behavioral Risk Factor Surveillance System
3.  BioSense 2.0 
Objective
To familiarize public health practitioners with the BioSense 2.0 application and its use in all hazard surveillance.
Introduction
BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration.
BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. In 2010, the BioSense Program began a redesign effort to improve features such as centralized data mining and addressing concerns that the system could not meet its original objective to provide early warning or detect local outbreaks.
Methods
Using the latest technology, BioSense 2.0 integrates current health data shared by health departments from a variety of sources to provide insight on the health of communities and the country. By getting more information faster, local, state, and federal public health partners can detect and respond to more outbreaks and health events more quickly. From flu outbreaks to car accidents, BioSense 2.0 provides the critical data, information, and tools that public health officials need to better understand and address health problems at the local, state, regional, and national levels. Also, by knowing what is happening across local borders, public health professionals can anticipate potential health problems and respond effectively to protect the health of all people.
The demonstration will include a basic overview of the BioSense 2.0 application and the functionality available to public health departments and their data providers. The presenter will also show an example of how BioSense 2.0 can be used in a real-world public health example.
Conclusions
Over the past two years much has been accomplished during the redesign effort. BioSense 2.0 was launched in November of 2011 and the collaboration between the BioSense program and the public health community has yielded an application based on a user-centered design approach and built on a platform that allows for flexible data sharing across jurisdictions and with partners. The public health community has played a critical role in designing and improving the BioSense 2.0 application and through continued collaboration the system will continue to improve.
Innovative features of the BioSense 2.0 application include the use of cloud technology, a novel and flexible data sharing feature, a community driven approach, enhanced algorithms, and no cost statistical analysis tools available in the cloud. Each of these features will be discussed during the presentation.
PMCID: PMC3692855
Syndromic Surveillance; Informatics; Situation Awareness
4.  Implementation of a Mobile-Based Surveillance System in Saudi Arabia for the 2009 Hajj 
Objective
To develop and implement a mobile-based disease surveillance system in the Kingdom of Saudi Arabia (KSA) for the 2009 Hajj; to strengthen public health preparedness for the H1N1 Influenza A pandemic.
Introduction
The Hajj is considered to be the largest mass gathering to date, attracting an estimated 2.5 million Muslims from more than 160 countries annually (1). The H1N1 Influenza A pandemic of 2009 generated a global wave of concern among public health departments that resulted in the institution of preventive measures to limit transmission of the disease. Meanwhile, the pandemic amplified an urgent need for more innovative disease surveillance tools to combat disease outbreaks.
A collaborative effort between the KSA Ministry of Health (MOH) and the U.S. Centers for Disease Control and Prevention (CDC) was initiated to implement and deploy an informatics-based mobile solution to provide early detection and reporting of disease outbreaks during the 2009 Hajj. The mobile-based tool aimed to improve the efficiency of disease case reporting, recognize potential outbreaks, and enhance the MOH’s operational effectiveness in deploying resources (2).
Methods
We designed a case-based system consisting of a mobile-based data collection toolkit and interactive map-based user interface to perform geospatial analysis and visualization. A train-the-trainer approach was adapted to provide training to the KSA MOH.
Results
More than 200 public health and information and communication technology (ICT) professionals were trained, and 100 mobile devices were deployed during the 2009 Hajj. Nine diseases and conditions that were considered as highest priority during the Hajj were under surveillance, including H1N1 Influenza A and Influenza-like Illness.
Pilot testing of the system was conducted during the first week of Ramadan and a modified system was fully operational during the Hajj. Data collected on smartphones were sent to the system via a secured network. The data were processed immediately and visualized on highly interactive maps with local and global views.
Conclusions
Effective public health decision-making requires timely and accurate information from a variety of sources. Mobile-based systems (e.g., personal digital assistants and smartphones) for data collection, transmission, reporting, and analyses provide a faster, easier, and cheaper means to communicate standardized and shareable public health data for decision-making (3). Mobile-based systems have been recognized as a quick and effective response solution to mass gatherings and recommended as data gathering and communication systems with geographical information system (GIS) capability (2). This paper explored the development and implementation of the Global Positioning System/ Geographic Information System (GPS/GIS) enabled mobile-based disease surveillance system as a feasible and effective way to support and strengthen preparedness for H1N1 Influenza A during the 2009 Hajj.
Mobile computing technology can be utilized to provide rapid and accurate data collection for public health decision-making during mass gatherings. The GIS-based interactive mapping tool provided a pioneering example of the power of a geographically based internet-accessible surveillance system with real-time data visualization. The technical challenges in the process of implementation and in the field were also identified.
A need now exists for a comprehensive and comparative review of parameters such as handheld device cost, training required, and system evaluations because selecting the appropriate software/hardware and system remains a challenge not only to public health professionals, but to the development and application of informatics technology as well.
PMCID: PMC3692784
Mobile Technology; GIS/GPS; Mass Gatherings; Surveillance System; Public Health Preparedness
5.  Utility of Syndromic Surveillance Using Novel Clinical Data Sources 
Objective
To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emergency department, urgent care clinic, hospital inpatient, and ambulatory clinical care data.
Introduction
Historically, syndromic surveillance has primarily involved the use of near real-time data sent from hospital emergency department (EDs) and urgent care (UC) clinics to public health agencies. The use of data from inpatient and ambulatory settings is now gaining interest and support throughout the United States, largely as a result of the Stage 2 and 3 Meaningful Use regulations [1]. Questions regarding the feasibility and utility of applying a syndromic approach to these data sources are hampering the development of systems to collect, analyze, and share this potentially valuable information. Solidifying the evidence base and communicating the results to the public health surveillance community may help to initiate and build support for using these data to advance surveillance functions.
Methods
We conducted a literature search in the published and grey literature that scanned for relevant articles in the Google Scholar, Pub Med, and EBSCO Information Services databases. Search terms included: “inpatient/ambulatory electronic health record”; “ambulatory/inpatient/hospital/outpatient/chronic disease syndromic surveillance”; and “EHR syndromic surveillance”. Information gleaned from each article included data use, data elements extracted, and data quality indicators. In addition, several stakeholders who provided input on the September 2012 ISDS Recommendations [2] also provided articles that were incorporated into the literature review.
ISDS also invited speakers from existing inpatient and ambulatory syndromic surveillance systems to give webinar presentations on how they are using data from these novel sources.
Results
The number of public health agencies (PHAs) routinely receiving ambulatory and inpatient syndromic surveillance data is substantially smaller than the number receiving ED and UC data. Some health departments, private medical organizations (including HMOs), and researchers are conducting syndromic surveillance and related research with health data captured in these clinical settings [2].
In inpatient settings, many of the necessary infrastructure and analytic tools are already in place. Syndromic surveillance with inpatient data has been used for a range of innovative uses, from monitoring trends in myocardial infarction in association with risk factors for cardiovascular disease [3] to tracking changes in incident-related hospitalizations following the 2011 Joplin, Missouri tornado [3].
In contrast, ambulatory systems face a need for new infrastructure, as well as pose a data volume challenge. The existing systems vary in how they address data volume and what types of encounters they capture. Ambulatory data has been used for a variety of uses, from monitoring gastrointestinal infectious disease [3], to monitoring behavioral health trends in a population, while protecting personal identities [4].
Conclusions
The existing syndromic surveillance systems and substantial research in the area indicate an interest in the public health community in using hospital inpatient and ambulatory clinical care data in new and innovative ways. However, before inpatient and ambulatory syndromic surveillance systems can be effectively utilized on a large scale, the gaps in knowledge and the barriers to system development must be addressed. Though the potential use cases are well documented, the generalizability to other settings requires additional research, workforce development, and investment.
PMCID: PMC3692877
Syndromic surveillance; EHR; Meaningful Use
6.  A Health Department’s Collaborative Model for Disease Surveillance Capacity Building 
Objective
Highlight one academic health department’s unique approach to optimizing collaborative opportunities for capacity development and document the implications for chronic disease surveillance and population health.
Introduction
Public Health departments are increasingly called upon to be innovative in quality service delivery under a dwindling resource climate as highlighted in several publications of the Institute of Medicine. Collaboration with other entities in the delivery of core public health services has emerged as a recurring theme. One model of this collaboration is an academic health department: a formal affiliation between a health professions school and a local health department. Initially targeted at workforce development, this model of collaboration has since yielded dividends in other core public health service areas including community assessment, program evaluation, community-based participatory research and data analysis.
The Duval County Health Department (DCHD), Florida, presents a unique community-centered model of the academic health department. Prominence in local informatics infrastructure capacity building and hosting a CDC-CSTE applied public health informatics fellowship (APHIF) in the Institute for Public Health Informatics and Research (IPHIR) in partnership with the Center for Health Equity Research, University of Florida & Shands medical center are direct dividends of this collaborative model.
Methods
We examined the collaborative efforts of the DCHD and present the unique advantages these have brought in the areas of entrenched data-driven public health service culture, community assessments, program evaluation, community-based participatory research and health informatics projects.
Results
Advantages of the model include a data-driven culture with the balanced scorecard model in leadership and sub-departmental emphases on quality assurance in public health services. Activities in IPHIR include data-driven approaches to program planning and grant developments, program evaluations, data analyses and impact assessments for the DCHD and other community health stakeholders.
Reports developed by IPHIR have impacted policy formulation by highlighting the need for sub county level data differentiation to address health disparities. Unique community-based mapping of Duval County into health zones based on health risk factors correlating with health outcome measures have been published. Other reports highlight chronic disease surveillance data and health scorecards in special populations.
Partnerships with regional higher institutions (University of Florida, University of North Florida and Florida A&M University) increased public health service delivery and yielded rich community-based participatory research opportunities.
Cutting edge participation in health IT policy implementation led to the hosting of the fledgling community HIE, the Jacksonville Health Information Network, as well as leadership in shaping the landscape of the state HIE. This has immense implications for public health surveillance activities as chronic disease surveillance and public health service research take center stage under new healthcare payment models amidst increasing calls for quality assurance in public health services.
DCHD is currently hosting a CDC-funded fellowship in applied public health informatics. Some of the projects materializing from the fellowship are the mapping of the current public health informatics profile of the DCHD, a community based diabetes disease registry to aid population-based management and surveillance of diabetes, development of a proposal for a combined primary care/general preventive medicine residency in UF-Shands Medical Center, Jacksonville and mobilization of DCHD healthcare providers for the roll-out of the state-built electronic medical records system (Florida HMS-EHR).
Conclusions
Academic health centers provide a model of collaboration that directly impacts on their success in delivering core public health services. Disease surveillance is positively affected by the diverse community affiliations of an academic health department. The academic health department, as epitomized by DCHD, is also better positioned to seize up-coming opportunities for local public health capacity building.
PMCID: PMC3692891
Academic Health Departments; collaborative model; health informatics projects
7.  A Type 2 Diabetes Prevention Website for African Americans, Caucasians, and Mexican Americans: Formative Evaluation 
JMIR Research Protocols  2013;2(2):e24.
Background
The majority of Americans now access the Internet, thereby expanding prospects for Web-based health-related education and intervention. However, there remains a digital divide among those with lower income and education, and among Spanish-speaking populations in the United States. Additional concerns are the low eHealth literacy rate among these populations and their interest in Internet-delivered interventions with these components. Given these factors, combined with the prevalence of type 2 diabetes among low socioeconomic status and Spanish-speaking Americans, strides need to be taken to reach these populations with online tools for diabetes prevention and management that are at once accessible and efficacious.
Objective
Using a formative evaluation of an eHealth diabetes prevention and control website, we tested the extent to which African Americans, Caucasians, and Mexican Americans at risk for type 2 diabetes gained knowledge and intended to modify their dietary intake and physical activity subsequent to viewing the website. We also examined their general Internet use patterns related to type 2 diabetes.
Methods
A mixed methods approach was undertaken. The diabetes prevention and control website provided educational and behavioral change information in English and Spanish. For this study, eligible participants (1) completed a prequantitative survey, (2) interacted with the website, (3) completed a qualitative interview, and (4) completed a postquantitative survey.
Results
After finding a significant differences in posttest diabetes knowledge scores (P<.001), a regression analysis controlling for pretest score, health literacy, ethnicity, Transtheoretical Model Stage for exercise and fruit and vegetable consumption, and Internet literacy was conducted. Internet literacy score (P=.04) and fruit and vegetable consumption stage (P<.001) were significantly associated with posttest scores indicating that those in precontemplation stage and with low Internet literacy scores were less likely to show improved diabetes knowledge scores. We found significant difference in posttest intention to eat a healthy diet each day in the next 2 months after controlling for pretest score, health literacy, ethnicity, Transtheoretical Model Stage for fruit and vegetable consumption and Internet literacy. Those in the Action stage of the Transtheoretical model for exercise were significantly less likely (P=.023) to improve the posttest score for intention to eat a healthy diet compared to those in the Preparation stage for exercise. We also found that health information is sought commonly across ethnic groups, but that diabetes-related information is less commonly sought even among those at risk. Other specific ethnic usage patterns were identified in the qualitative data including content sought on Web searches and technology used to access the Internet.
Conclusions
This study provides in-depth qualitative insight into the seeking, access, and use of Web-based health information across three ethnic groups in two languages. Additionally, it provides evidence from pre-post measures of exposure to Web-based health content and related changes in diabetes knowledge and intention to eat a healthy diet.
doi:10.2196/resprot.2573
PMCID: PMC3713918  PMID: 23846668
diabetes; Internet; Mexican-Americans; African Americans; socioeconomic status; dietary intake; physical activity; health literacy; website
8.  Evaluation of Clinical and Administrative Data to Augment Public Health Surveillance 
Objective
To assess the utility of inpatient and ambulatory clinical data compiled by public and commercial sources to enhance the Centers for Disease Control and Prevention’s surveillance activities.
Introduction
Medical claims and EHR data sources offer the potential to ascertain disease and health risk behavior prevalence and incidence, evaluate the use of clinical services, and monitor changes related to public health interventions. Passage of the HITECH Act of 2009 supports the availability of standardized EHR data for use by public health officials to obtain actionable information. While full adoption of EHRs is still years away, there are presently publicly- and commercially-available EHR and medical claims data sets that could enhance public health surveillance at a national, regional and state level. The purposes of this evaluation were to i.) demonstrate the feasibility of gaining access to such data, ii.) evaluate their ability to augment current surveillance activities by developing measures for twenty separate healthcare indicators (e.g., HIV screening), iii.) evaluate each data source across a set of criteria needed for an effective surveillance system, and iv.) assess the ability of the data sources to evaluate changes in healthcare utilization and preventive services that may be a result of the 2009 Health Reform legislation.
Methods
Ten separate data sources were selected for inclusion in the study based on a number of criteria, including availability, representativeness, population, data structure and content, cost, and longitudinality. In collaboration with staff from seven Divisions across the CDC, detailed specifications were developed for twenty separate indicators of healthcare utilization or preventive services using best practices in healthcare quality measurement. Specifications were developed separately for EHR and medical claims data due to their differing structure, content and use of medical code sets and terminologies. Specifications for EHR data sources relied on the National Quality Forum (NQF) Meaningful Use (MUse) clinical quality measure specifications. The use of NQF MUse specification guidelines allowed us to gauge the current ability of each data source to measure healthcare utilization and preventive services as recommended by NQF, the national leader in healthcare measurement. Each of the data sources was also evaluated across established public health surveillance criteria, including data quality, representativeness, and flexibility, among others. Data analysis was performed using SAS 9.3 (SAS Institute, Cary, NC).
Results
All twenty of the healthcare indicators were developed for at least one data source; however, many of the indicator specifications had to be modified due to the low frequency of certain code sets (e.g., CPT-4 II, LOINC). The observed strengths of medical claims data were the relatively low cost, ability to track patients longitudinally, and the standardized representation of procedures and diagnoses through use of medical codes, such as ICD-9-CM, CPT-4 and HCPCS. The observed strengths of EHR data sources were the availability of information related to health behavior (e.g., current smoker), health assessment (e.g., BMI), prognostic indicators (e.g., vital signs, laboratory result), diagnostic testing, and functional status. While EHR data also capture diagnoses using ICD-9-CM, procedures such as medical and laboratory procedures remain documented through use of free text or semi-structured text fields, making it difficult to process.
Conclusions
Currently available healthcare data can improve the timeliness of health outcome monitoring and add complementary information on healthcare utilization to improve our interpretation of traditional public health surveillance data. Medical claims data support measurement of health outcomes and healthcare services provided to patient populations; however, without clinical encounter information, they cannot develop measures estimating the impact of services received on quality of care. EHR data have richer clinical information; however, the continued use of non-standards-based medical codes and free and semi-structured text fields make it difficult to analyze data at scale. Meaningful Use and other HITECH initiatives are changing this by incentivizing the standardization and aggregation of electronic healthcare data. In time, these data may yield timely, accurate and actionable information for public health surveillance.
PMCID: PMC3692874
Surveillance; Evaluation; Healthcare; Electronic Health Record
9.  Inflammation, Insulin Resistance, and Diabetes—Mendelian Randomization Using CRP Haplotypes Points Upstream 
PLoS Medicine  2008;5(8):e155.
Background
Raised C-reactive protein (CRP) is a risk factor for type 2 diabetes. According to the Mendelian randomization method, the association is likely to be causal if genetic variants that affect CRP level are associated with markers of diabetes development and diabetes. Our objective was to examine the nature of the association between CRP phenotype and diabetes development using CRP haplotypes as instrumental variables.
Methods and Findings
We genotyped three tagging SNPs (CRP + 2302G > A; CRP + 1444T > C; CRP + 4899T > G) in the CRP gene and measured serum CRP in 5,274 men and women at mean ages 49 and 61 y (Whitehall II Study). Homeostasis model assessment-insulin resistance (HOMA-IR) and hemoglobin A1c (HbA1c) were measured at age 61 y. Diabetes was ascertained by glucose tolerance test and self-report. Common major haplotypes were strongly associated with serum CRP levels, but unrelated to obesity, blood pressure, and socioeconomic position, which may confound the association between CRP and diabetes risk. Serum CRP was associated with these potential confounding factors. After adjustment for age and sex, baseline serum CRP was associated with incident diabetes (hazard ratio = 1.39 [95% confidence interval 1.29–1.51], HOMA-IR, and HbA1c, but the associations were considerably attenuated on adjustment for potential confounding factors. In contrast, CRP haplotypes were not associated with HOMA-IR or HbA1c (p = 0.52–0.92). The associations of CRP with HOMA-IR and HbA1c were all null when examined using instrumental variables analysis, with genetic variants as the instrument for serum CRP. Instrumental variables estimates differed from the directly observed associations (p = 0.007–0.11). Pooled analysis of CRP haplotypes and diabetes in Whitehall II and Northwick Park Heart Study II produced null findings (p = 0.25–0.88). Analyses based on the Wellcome Trust Case Control Consortium (1,923 diabetes cases, 2,932 controls) using three SNPs in tight linkage disequilibrium with our tagging SNPs also demonstrated null associations.
Conclusions
Observed associations between serum CRP and insulin resistance, glycemia, and diabetes are likely to be noncausal. Inflammation may play a causal role via upstream effectors rather than the downstream marker CRP.
Using a Mendelian randomization approach, Eric Brunner and colleagues show that the associations between serum C-reactive protein and insulin resistance, glycemia, and diabetes are likely to be noncausal.
Editors' Summary
Background.
Diabetes—a common, long-term (chronic) disease that causes heart, kidney, nerve, and eye problems and shortens life expectancy—is characterized by high levels of sugar (glucose) in the blood. In people without diabetes, blood sugar levels are controlled by the hormone insulin. Insulin is released by the pancreas after eating and “instructs” insulin-responsive muscle and fat cells to take up the glucose from the bloodstream that is produced by the digestion of food. In the early stages of type 2 diabetes (the commonest type of diabetes), the muscle and fat cells become nonresponsive to insulin (a condition called insulin resistance), and blood sugar levels increase. The pancreas responds by making more insulin—people with insulin resistance have high blood levels of both insulin and glucose. Eventually, however, the insulin-producing cells in the pancreas start to malfunction, insulin secretion decreases, and frank diabetes develops.
Why Was This Study Done?
Globally, about 200 million people have diabetes, but experts believe this number will double by 2030. Ways to prevent or delay the onset of diabetes are, therefore, urgently needed. One major risk factor for insulin resistance and diabetes is being overweight. According to one theory, increased body fat causes mild, chronic tissue inflammation, which leads to insulin resistance. Consistent with this idea, people with higher than normal amounts of the inflammatory protein C-reactive protein (CRP) in their blood have a high risk of developing diabetes. If inflammation does cause diabetes, then drugs that inhibit CRP might prevent diabetes. However, simply measuring CRP and determining whether the people with high levels develop diabetes cannot prove that CRP causes diabetes. Those people with high blood levels of CRP might have other unknown factors in common (confounding factors) that are the real causes of diabetes. In this study, the researchers use “Mendelian randomization” to examine whether increased blood CRP causes diabetes. Some variants of CRP (the gene that encodes CRP) increase the amount of CRP in the blood. Because these variants are inherited randomly, there is no likelihood of confounding factors, and an association between these variants and the development of insulin resistance and diabetes indicates, therefore, that increased CRP levels cause diabetes.
What Did the Researchers Do and Find?
The researchers measured blood CRP levels in more than 5,000 people enrolled in the Whitehall II study, which is investigating factors that affect disease development. They also used the “homeostasis model assessment-insulin resistance” (HOMA-IR) method to estimate insulin sensitivity from blood glucose and insulin measurements, and measured levels of hemoglobin A1c (HbA1c, hemoglobin with sugar attached—a measure of long-term blood sugar control) in these people. Finally, they looked at three “single polynucleotide polymorphisms” (SNPs, single nucleotide changes in a gene's DNA sequence; combinations of SNPs that are inherited as a block are called haplotypes) in CRP in each study participant. Common haplotypes of CRP were related to blood serum CRP levels and, as previously reported, increased blood CRP levels were associated with diabetes and with HOMA-IR and HbA1c values indicative of insulin resistance and poor blood sugar control, respectively. By contrast, CRP haplotypes were not related to HOMA-IR or HbA1c values. Similarly, pooled analysis of CRP haplotypes and diabetes in Whitehall II and another large study on health determinants (the Northwick Park Heart Study II) showed no association between CRP variants and diabetes risk. Finally, data from the Wellcome Trust Case Control Consortium also showed no association between CRP haplotypes and diabetes risk.
What Do These Findings Mean?
Together, these findings suggest that increased blood CRP levels are not responsible for the development of insulin resistance or diabetes, at least in European populations. It may be that there is a causal relationship between CRP levels and diabetes risk in other ethnic populations—further Mendelian randomization studies are needed to discover whether this is the case. For now, though, these findings suggest that drugs targeted against CRP are unlikely to prevent or delay the onset of diabetes. However, they do not discount the possibility that proteins involved earlier in the inflammatory process might cause diabetes and might thus represent good drug targets for diabetes prevention.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050155.
This study is further discussed in a PLoS Medicine Perspective by Bernard Keavney
The MedlinePlus encyclopedia provides information about diabetes and about C-reactive protein (in English and Spanish)
US National Institute of Diabetes and Digestive and Kidney Diseases provides patient information on all aspects of diabetes, including information on insulin resistance (in English and Spanish)
The International Diabetes Federation provides information about diabetes, including information on the global diabetes epidemic
The US Centers for Disease Control and Prevention provides information for the public and professionals on all aspects of diabetes (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.0050155
PMCID: PMC2504484  PMID: 18700811
10.  Association of Lifecourse Socioeconomic Status with Chronic Inflammation and Type 2 Diabetes Risk: The Whitehall II Prospective Cohort Study 
PLoS Medicine  2013;10(7):e1001479.
Silvia Stringhini and colleagues followed a group of British civil servants over 18 years to look for links between socioeconomic status and health.
Please see later in the article for the Editors' Summary
Background
Socioeconomic adversity in early life has been hypothesized to “program” a vulnerable phenotype with exaggerated inflammatory responses, so increasing the risk of developing type 2 diabetes in adulthood. The aim of this study is to test this hypothesis by assessing the extent to which the association between lifecourse socioeconomic status and type 2 diabetes incidence is explained by chronic inflammation.
Methods and Findings
We use data from the British Whitehall II study, a prospective occupational cohort of adults established in 1985. The inflammatory markers C-reactive protein and interleukin-6 were measured repeatedly and type 2 diabetes incidence (new cases) was monitored over an 18-year follow-up (from 1991–1993 until 2007–2009). Our analytical sample consisted of 6,387 non-diabetic participants (1,818 women), of whom 731 (207 women) developed type 2 diabetes over the follow-up. Cumulative exposure to low socioeconomic status from childhood to middle age was associated with an increased risk of developing type 2 diabetes in adulthood (hazard ratio [HR] = 1.96, 95% confidence interval: 1.48–2.58 for low cumulative lifecourse socioeconomic score and HR = 1.55, 95% confidence interval: 1.26–1.91 for low-low socioeconomic trajectory). 25% of the excess risk associated with cumulative socioeconomic adversity across the lifecourse and 32% of the excess risk associated with low-low socioeconomic trajectory was attributable to chronically elevated inflammation (95% confidence intervals 16%–58%).
Conclusions
In the present study, chronic inflammation explained a substantial part of the association between lifecourse socioeconomic disadvantage and type 2 diabetes. Further studies should be performed to confirm these findings in population-based samples, as the Whitehall II cohort is not representative of the general population, and to examine the extent to which social inequalities attributable to chronic inflammation are reversible.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, a metabolic disorder characterized by high amounts of glucose (sugar) in the blood. Blood sugar levels are normally controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes) blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes, which was previously called adult-onset diabetes, can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. However, as the disease progresses, the pancreatic beta cells, which make insulin, become impaired and patients may eventually need insulin injections. Long-term complications, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
Socioeconomic adversity in childhood seems to increase the risk of developing type 2 diabetes but why? One possibility is that chronic inflammation mediates the association between socioeconomic adversity and type 2 diabetes. Inflammation, which is the body's normal response to injury and disease, affects insulin signaling and increases beta-cell death, and markers of inflammation such as raised blood levels of C-reactive protein and interleukin 6 are associated with future diabetes risk. Notably, socioeconomic adversity in early life leads to exaggerated inflammatory responses later in life and people exposed to social adversity in adulthood show greater levels of inflammation than people with a higher socioeconomic status. In this prospective cohort study (an investigation that records the baseline characteristics of a group of people and then follows them to see who develops specific conditions), the researchers test the hypothesis that chronically increased inflammatory activity in individuals exposed to socioeconomic adversity over their lifetime may partly mediate the association between socioeconomic status over the lifecourse and future type 2 diabetes risk.
What Did the Researchers Do and Find?
To assess the extent to which chronic inflammation explains the association between lifecourse socioeconomic status and type 2 diabetes incidence (new cases), the researchers used data from the Whitehall II study, a prospective occupational cohort study initiated in 1985 to investigate the mechanisms underlying previously observed socioeconomic inequalities in disease. Whitehall II enrolled more than 10,000 London-based government employees ranging from clerical/support staff to administrative officials and monitored inflammatory marker levels and type 2 diabetes incidence in the study participants from 1991–1993 until 2007–2009. Of 6,387 participants who were not diabetic in 1991–1993, 731 developed diabetes during the 18-year follow-up. Compared to participants with the highest cumulative lifecourse socioeconomic score (calculated using information on father's occupational position and the participant's educational attainment and occupational position), participants with the lowest score had almost double the risk of developing diabetes during follow-up. Low lifetime socioeconomic status trajectories (being socially downwardly mobile or starting and ending with a low socioeconomic status) were also associated with an increased risk of developing diabetes in adulthood. A quarter of the excess risk associated with cumulative socioeconomic adversity and nearly a third of the excess risk associated with low socioeconomic trajectory was attributable to chronically increased inflammation.
What Do These Findings Mean?
These findings show a robust association between adverse socioeconomic circumstances over the lifecourse of the Whitehall II study participants and the risk of type 2 diabetes and suggest that chronic inflammation explains up to a third of this association. The accuracy of these findings may be affected by the measures of socioeconomic status used in the study. Moreover, because the study participants were from an occupational cohort, these findings need to be confirmed in a general population. Studies are also needed to examine the extent to which social inequalities in diabetes risk that are attributable to chronic inflammation are reversible. Importantly, if future studies confirm and extend the findings reported here, it might be possible to reduce the social inequalities in type 2 diabetes by promoting interventions designed to reduce inflammation, including weight management, physical activity, and smoking cessation programs and the use of anti-inflammatory drugs, among socially disadvantaged groups.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001479.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals, and the general public, including information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes; it includes peoples stories about diabetes
The nonprofit Diabetes UK also provides detailed information about diabetes for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the nonprofit Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
Information about the Whitehall II study is available
doi:10.1371/journal.pmed.1001479
PMCID: PMC3699448  PMID: 23843750
11.  Disease Surveillance and Achieving Synergy In Public Health Quality Improvement 
Objective
To examine disease surveillance in the context of a new national framework for public health quality and to solicit input from practitioners, researchers, and other stakeholders to identify potential metrics, pivotal research questions, and actions for achieving synergy between surveillance practice and public health quality.
Introduction
National efforts to improve quality in public health are closely tied to advancing capabilities in disease surveillance. Measures of public health quality provide data to demonstrate how public health programs, services, policies, and research achieve desired health outcomes and impact population health. They also reveal opportunities for innovations and improvements. Similar quality improvement efforts in the health care system are beginning to bear fruit. There has been a need, however, for a framework for assessing public health quality that provides a standard, yet is flexible and relevant to agencies at all levels.
The U.S. Health and Human Services (HHS) Office of the Assistant Secretary for Health, working with stakeholders, recently developed and released a Consensus Statement on Quality in the Public Health System that introduces a novel evaluation framework. They identified nine aims that are fundamental to public health quality improvement efforts and six cross-cutting priority areas for improvement, including population health metrics and information technology; workforce development; and evidence-based practices (1).
Applying the HHS framework to surveillance expands measures for surveillance quality beyond typical variables (e.g., data quality and analytic capabilities) to desired characteristics of a quality public health system. The question becomes: How can disease surveillance help public health services to be more population centered, equitable, proactive, health-promoting, risk-reducing, vigilant, transparent, effective, and efficient—the desired features of a quality public health system?
Any agency with a public health mission, or even a partial public health mission (e.g., tax-exempt hospitals), can use these measures to develop strategies that improve both the quality of the surveillance enterprise and public health systems, overall. At this time, input from stakeholders is needed to identify valid and feasible ways to measure how surveillance systems and practices advance public health quality. What exists now and where are the gaps?
Methods
Improving public health by applying quality measures to disease surveillance will require innovation and collaboration among stakeholders. This roundtable will begin a community dialogue to spark this process. The first goal will be to achieve a common focus by defining the nine quality aims identified in the HHS Consensus Statement. Attendees will draw from their experience to discuss how surveillance practice advances the public health aims and improves public health. We will also identify key research questions needed to provide evidence to inform decision-making.
Results
The roundtable will discuss how the current state of surveillance practice addresses each of the aims described in the Consensus Statement to create a snapshot of how surveillance contributes to public health quality and begin to articulate practical measures for assessing quality improvements. Sample questions to catalyze discussion include: —How is surveillance used to identify and address health disparities and, thereby, make public health more equitable? What are the data sources? Are there targets? How can research and evaluation help to enhance this surveillance capability and direct action?—How do we identify and address factors that inhibit quality improvement in surveillance? What are the gaps in knowledge, skills, systems, and resources?—Where can standardization play a positive role in the evaluation of quality in public health surveillance?—How can we leverage resources by aligning national, state, and local goals? —What are the key research questions and the quality improvement projects that can be implemented using recognized models for improvement?—How can syndromic surveillance, specifically, advance the priority aims?
The roundtable will conclude with a list of next steps to develop metrics that resonate with the business practices of public health at all levels.
PMCID: PMC3692848
public health quality; metrics; framework
12.  Internet-Based Device-Assisted Remote Monitoring of Cardiovascular Implantable Electronic Devices 
Executive Summary
Objective
The objective of this Medical Advisory Secretariat (MAS) report was to conduct a systematic review of the available published evidence on the safety, effectiveness, and cost-effectiveness of Internet-based device-assisted remote monitoring systems (RMSs) for therapeutic cardiac implantable electronic devices (CIEDs) such as pacemakers (PMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy (CRT) devices. The MAS evidence-based review was performed to support public financing decisions.
Clinical Need: Condition and Target Population
Sudden cardiac death (SCD) is a major cause of fatalities in developed countries. In the United States almost half a million people die of SCD annually, resulting in more deaths than stroke, lung cancer, breast cancer, and AIDS combined. In Canada each year more than 40,000 people die from a cardiovascular related cause; approximately half of these deaths are attributable to SCD.
Most cases of SCD occur in the general population typically in those without a known history of heart disease. Most SCDs are caused by cardiac arrhythmia, an abnormal heart rhythm caused by malfunctions of the heart’s electrical system. Up to half of patients with significant heart failure (HF) also have advanced conduction abnormalities.
Cardiac arrhythmias are managed by a variety of drugs, ablative procedures, and therapeutic CIEDs. The range of CIEDs includes pacemakers (PMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy (CRT) devices. Bradycardia is the main indication for PMs and individuals at high risk for SCD are often treated by ICDs.
Heart failure (HF) is also a significant health problem and is the most frequent cause of hospitalization in those over 65 years of age. Patients with moderate to severe HF may also have cardiac arrhythmias, although the cause may be related more to heart pump or haemodynamic failure. The presence of HF, however, increases the risk of SCD five-fold, regardless of aetiology. Patients with HF who remain highly symptomatic despite optimal drug therapy are sometimes also treated with CRT devices.
With an increasing prevalence of age-related conditions such as chronic HF and the expanding indications for ICD therapy, the rate of ICD placement has been dramatically increasing. The appropriate indications for ICD placement, as well as the rate of ICD placement, are increasingly an issue. In the United States, after the introduction of expanded coverage of ICDs, a national ICD registry was created in 2005 to track these devices. A recent survey based on this national ICD registry reported that 22.5% (25,145) of patients had received a non-evidence based ICD and that these patients experienced significantly higher in-hospital mortality and post-procedural complications.
In addition to the increased ICD device placement and the upfront device costs, there is the need for lifelong follow-up or surveillance, placing a significant burden on patients and device clinics. In 2007, over 1.6 million CIEDs were implanted in Europe and the United States, which translates to over 5.5 million patient encounters per year if the recommended follow-up practices are considered. A safe and effective RMS could potentially improve the efficiency of long-term follow-up of patients and their CIEDs.
Technology
In addition to being therapeutic devices, CIEDs have extensive diagnostic abilities. All CIEDs can be interrogated and reprogrammed during an in-clinic visit using an inductive programming wand. Remote monitoring would allow patients to transmit information recorded in their devices from the comfort of their own homes. Currently most ICD devices also have the potential to be remotely monitored. Remote monitoring (RM) can be used to check system integrity, to alert on arrhythmic episodes, and to potentially replace in-clinic follow-ups and manage disease remotely. They do not currently have the capability of being reprogrammed remotely, although this feature is being tested in pilot settings.
Every RMS is specifically designed by a manufacturer for their cardiac implant devices. For Internet-based device-assisted RMSs, this customization includes details such as web application, multiplatform sensors, custom algorithms, programming information, and types and methods of alerting patients and/or physicians. The addition of peripherals for monitoring weight and pressure or communicating with patients through the onsite communicators also varies by manufacturer. Internet-based device-assisted RMSs for CIEDs are intended to function as a surveillance system rather than an emergency system.
Health care providers therefore need to learn each application, and as more than one application may be used at one site, multiple applications may need to be reviewed for alarms. All RMSs deliver system integrity alerting; however, some systems seem to be better geared to fast arrhythmic alerting, whereas other systems appear to be more intended for remote follow-up or supplemental remote disease management. The different RMSs may therefore have different impacts on workflow organization because of their varying frequency of interrogation and methods of alerts. The integration of these proprietary RM web-based registry systems with hospital-based electronic health record systems has so far not been commonly implemented.
Currently there are 2 general types of RMSs: those that transmit device diagnostic information automatically and without patient assistance to secure Internet-based registry systems, and those that require patient assistance to transmit information. Both systems employ the use of preprogrammed alerts that are either transmitted automatically or at regular scheduled intervals to patients and/or physicians.
The current web applications, programming, and registry systems differ greatly between the manufacturers of transmitting cardiac devices. In Canada there are currently 4 manufacturers—Medtronic Inc., Biotronik, Boston Scientific Corp., and St Jude Medical Inc.—which have regulatory approval for remote transmitting CIEDs. Remote monitoring systems are proprietary to the manufacturer of the implant device. An RMS for one device will not work with another device, and the RMS may not work with all versions of the manufacturer’s devices.
All Internet-based device-assisted RMSs have common components. The implanted device is equipped with a micro-antenna that communicates with a small external device (at bedside or wearable) commonly known as the transmitter. Transmitters are able to interrogate programmed parameters and diagnostic data stored in the patients’ implant device. The information transfer to the communicator can occur at preset time intervals with the participation of the patient (waving a wand over the device) or it can be sent automatically (wirelessly) without their participation. The encrypted data are then uploaded to an Internet-based database on a secure central server. The data processing facilities at the central database, depending on the clinical urgency, can trigger an alert for the physician(s) that can be sent via email, fax, text message, or phone. The details are also posted on the secure website for viewing by the physician (or their delegate) at their convenience.
Research Questions
The research directions and specific research questions for this evidence review were as follows:
To identify the Internet-based device-assisted RMSs available for follow-up of patients with therapeutic CIEDs such as PMs, ICDs, and CRT devices.
To identify the potential risks, operational issues, or organizational issues related to Internet-based device-assisted RM for CIEDs.
To evaluate the safety, acceptability, and effectiveness of Internet-based device-assisted RMSs for CIEDs such as PMs, ICDs, and CRT devices.
To evaluate the safety, effectiveness, and cost-effectiveness of Internet-based device-assisted RMSs for CIEDs compared to usual outpatient in-office monitoring strategies.
To evaluate the resource implications or budget impact of RMSs for CIEDs in Ontario, Canada.
Research Methods
Literature Search
The review included a systematic review of published scientific literature and consultations with experts and manufacturers of all 4 approved RMSs for CIEDs in Canada. Information on CIED cardiac implant clinics was also obtained from Provincial Programs, a division within the Ministry of Health and Long-Term Care with a mandate for cardiac implant specialty care. Various administrative databases and registries were used to outline the current clinical follow-up burden of CIEDs in Ontario. The provincial population-based ICD database developed and maintained by the Institute for Clinical Evaluative Sciences (ICES) was used to review the current follow-up practices with Ontario patients implanted with ICD devices.
Search Strategy
A literature search was performed on September 21, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from 1950 to September 2010. Search alerts were generated and reviewed for additional relevant literature until December 31, 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search.
Inclusion Criteria
published between 1950 and September 2010;
English language full-reports and human studies;
original reports including clinical evaluations of Internet-based device-assisted RMSs for CIEDs in clinical settings;
reports including standardized measurements on outcome events such as technical success, safety, effectiveness, cost, measures of health care utilization, morbidity, mortality, quality of life or patient satisfaction;
randomized controlled trials (RCTs), systematic reviews and meta-analyses, cohort and controlled clinical studies.
Exclusion Criteria
non-systematic reviews, letters, comments and editorials;
reports not involving standardized outcome events;
clinical reports not involving Internet-based device assisted RM systems for CIEDs in clinical settings;
reports involving studies testing or validating algorithms without RM;
studies with small samples (<10 subjects).
Outcomes of Interest
The outcomes of interest included: technical outcomes, emergency department visits, complications, major adverse events, symptoms, hospital admissions, clinic visits (scheduled and/or unscheduled), survival, morbidity (disease progression, stroke, etc.), patient satisfaction, and quality of life.
Summary of Findings
The MAS evidence review was performed to review available evidence on Internet-based device-assisted RMSs for CIEDs published until September 2010. The search identified 6 systematic reviews, 7 randomized controlled trials, and 19 reports for 16 cohort studies—3 of these being registry-based and 4 being multi-centered. The evidence is summarized in the 3 sections that follow.
1. Effectiveness of Remote Monitoring Systems of CIEDs for Cardiac Arrhythmia and Device Functioning
In total, 15 reports on 13 cohort studies involving investigations with 4 different RMSs for CIEDs in cardiology implant clinic groups were identified in the review. The 4 RMSs were: Care Link Network® (Medtronic Inc,, Minneapolis, MN, USA); Home Monitoring® (Biotronic, Berlin, Germany); House Call 11® (St Jude Medical Inc., St Pauls, MN, USA); and a manufacturer-independent RMS. Eight of these reports were with the Home Monitoring® RMS (12,949 patients), 3 were with the Care Link® RMS (167 patients), 1 was with the House Call 11® RMS (124 patients), and 1 was with a manufacturer-independent RMS (44 patients). All of the studies, except for 2 in the United States, (1 with Home Monitoring® and 1 with House Call 11®), were performed in European countries.
The RMSs in the studies were evaluated with different cardiac implant device populations: ICDs only (6 studies), ICD and CRT devices (3 studies), PM and ICD and CRT devices (4 studies), and PMs only (2 studies). The patient populations were predominately male (range, 52%–87%) in all studies, with mean ages ranging from 58 to 76 years. One study population was unique in that RMSs were evaluated for ICDs implanted solely for primary prevention in young patients (mean age, 44 years) with Brugada syndrome, which carries an inherited increased genetic risk for sudden heart attack in young adults.
Most of the cohort studies reported on the feasibility of RMSs in clinical settings with limited follow-up. In the short follow-up periods of the studies, the majority of the events were related to detection of medical events rather than system configuration or device abnormalities. The results of the studies are summarized below:
The interrogation of devices on the web platform, both for continuous and scheduled transmissions, was significantly quicker with remote follow-up, both for nurses and physicians.
In a case-control study focusing on a Brugada population–based registry with patients followed-up remotely, there were significantly fewer outpatient visits and greater detection of inappropriate shocks. One death occurred in the control group not followed remotely and post-mortem analysis indicated early signs of lead failure prior to the event.
Two studies examined the role of RMSs in following ICD leads under regulatory advisory in a European clinical setting and noted:
– Fewer inappropriate shocks were administered in the RM group.
– Urgent in-office interrogations and surgical revisions were performed within 12 days of remote alerts.
– No signs of lead fracture were detected at in-office follow-up; all were detected at remote follow-up.
Only 1 study reported evaluating quality of life in patients followed up remotely at 3 and 6 months; no values were reported.
Patient satisfaction was evaluated in 5 cohort studies, all in short term follow-up: 1 for the Home Monitoring® RMS, 3 for the Care Link® RMS, and 1 for the House Call 11® RMS.
– Patients reported receiving a sense of security from the transmitter, a good relationship with nurses and physicians, positive implications for their health, and satisfaction with RM and organization of services.
– Although patients reported that the system was easy to implement and required less than 10 minutes to transmit information, a variable proportion of patients (range, 9% 39%) reported that they needed the assistance of a caregiver for their transmission.
– The majority of patients would recommend RM to other ICD patients.
– Patients with hearing or other physical or mental conditions hindering the use of the system were excluded from studies, but the frequency of this was not reported.
Physician satisfaction was evaluated in 3 studies, all with the Care Link® RMS:
– Physicians reported an ease of use and high satisfaction with a generally short-term use of the RMS.
– Physicians reported being able to address the problems in unscheduled patient transmissions or physician initiated transmissions remotely, and were able to handle the majority of the troubleshooting calls remotely.
– Both nurses and physicians reported a high level of satisfaction with the web registry system.
2. Effectiveness of Remote Monitoring Systems in Heart Failure Patients for Cardiac Arrhythmia and Heart Failure Episodes
Remote follow-up of HF patients implanted with ICD or CRT devices, generally managed in specialized HF clinics, was evaluated in 3 cohort studies: 1 involved the Home Monitoring® RMS and 2 involved the Care Link® RMS. In these RMSs, in addition to the standard diagnostic features, the cardiac devices continuously assess other variables such as patient activity, mean heart rate, and heart rate variability. Intra-thoracic impedance, a proxy measure for lung fluid overload, was also measured in the Care Link® studies. The overall diagnostic performance of these measures cannot be evaluated, as the information was not reported for patients who did not experience intra-thoracic impedance threshold crossings or did not undergo interventions. The trial results involved descriptive information on transmissions and alerts in patients experiencing high morbidity and hospitalization in the short study periods.
3. Comparative Effectiveness of Remote Monitoring Systems for CIEDs
Seven RCTs were identified evaluating RMSs for CIEDs: 2 were for PMs (1276 patients) and 5 were for ICD/CRT devices (3733 patients). Studies performed in the clinical setting in the United States involved both the Care Link® RMS and the Home Monitoring® RMS, whereas all studies performed in European countries involved only the Home Monitoring® RMS.
3A. Randomized Controlled Trials of Remote Monitoring Systems for Pacemakers
Two trials, both multicenter RCTs, were conducted in different countries with different RMSs and study objectives. The PREFER trial was a large trial (897 patients) performed in the United States examining the ability of Care Link®, an Internet-based remote PM interrogation system, to detect clinically actionable events (CAEs) sooner than the current in-office follow-up supplemented with transtelephonic monitoring transmissions, a limited form of remote device interrogation. The trial results are summarized below:
In the 375-day mean follow-up, 382 patients were identified with at least 1 CAE—111 patients in the control arm and 271 in the remote arm.
The event rate detected per patient for every type of CAE, except for loss of atrial capture, was higher in the remote arm than the control arm.
The median time to first detection of CAEs (4.9 vs. 6.3 months) was significantly shorter in the RMS group compared to the control group (P < 0.0001).
Additionally, only 2% (3/190) of the CAEs in the control arm were detected during a transtelephonic monitoring transmission (the rest were detected at in-office follow-ups), whereas 66% (446/676) of the CAEs were detected during remote interrogation.
The second study, the OEDIPE trial, was a smaller trial (379 patients) performed in France evaluating the ability of the Home Monitoring® RMS to shorten PM post-operative hospitalization while preserving the safety of conventional management of longer hospital stays.
Implementation and operationalization of the RMS was reported to be successful in 91% (346/379) of the patients and represented 8144 transmissions.
In the RM group 6.5% of patients failed to send messages (10 due to improper use of the transmitter, 2 with unmanageable stress). Of the 172 patients transmitting, 108 patients sent a total of 167 warnings during the trial, with a greater proportion of warnings being attributed to medical rather than technical causes.
Forty percent had no warning message transmission and among these, 6 patients experienced a major adverse event and 1 patient experienced a non-major adverse event. Of the 6 patients having a major adverse event, 5 contacted their physician.
The mean medical reaction time was faster in the RM group (6.5 ± 7.6 days vs. 11.4 ± 11.6 days).
The mean duration of hospitalization was significantly shorter (P < 0.001) for the RM group than the control group (3.2 ± 3.2 days vs. 4.8 ± 3.7 days).
Quality of life estimates by the SF-36 questionnaire were similar for the 2 groups at 1-month follow-up.
3B. Randomized Controlled Trials Evaluating Remote Monitoring Systems for ICD or CRT Devices
The 5 studies evaluating the impact of RMSs with ICD/CRT devices were conducted in the United States and in European countries and involved 2 RMSs—Care Link® and Home Monitoring ®. The objectives of the trials varied and 3 of the trials were smaller pilot investigations.
The first of the smaller studies (151 patients) evaluated patient satisfaction, achievement of patient outcomes, and the cost-effectiveness of the Care Link® RMS compared to quarterly in-office device interrogations with 1-year follow-up.
Individual outcomes such as hospitalizations, emergency department visits, and unscheduled clinic visits were not significantly different between the study groups.
Except for a significantly higher detection of atrial fibrillation in the RM group, data on ICD detection and therapy were similar in the study groups.
Health-related quality of life evaluated by the EuroQoL at 6-month or 12-month follow-up was not different between study groups.
Patients were more satisfied with their ICD care in the clinic follow-up group than in the remote follow-up group at 6-month follow-up, but were equally satisfied at 12- month follow-up.
The second small pilot trial (20 patients) examined the impact of RM follow-up with the House Call 11® system on work schedules and cost savings in patients randomized to 2 study arms varying in the degree of remote follow-up.
The total time including device interrogation, transmission time, data analysis, and physician time required was significantly shorter for the RM follow-up group.
The in-clinic waiting time was eliminated for patients in the RM follow-up group.
The physician talk time was significantly reduced in the RM follow-up group (P < 0.05).
The time for the actual device interrogation did not differ in the study groups.
The third small trial (115 patients) examined the impact of RM with the Home Monitoring® system compared to scheduled trimonthly in-clinic visits on the number of unplanned visits, total costs, health-related quality of life (SF-36), and overall mortality.
There was a 63.2% reduction in in-office visits in the RM group.
Hospitalizations or overall mortality (values not stated) were not significantly different between the study groups.
Patient-induced visits were higher in the RM group than the in-clinic follow-up group.
The TRUST Trial
The TRUST trial was a large multicenter RCT conducted at 102 centers in the United States involving the Home Monitoring® RMS for ICD devices for 1450 patients. The primary objectives of the trial were to determine if remote follow-up could be safely substituted for in-office clinic follow-up (3 in-office visits replaced) and still enable earlier physician detection of clinically actionable events.
Adherence to the protocol follow-up schedule was significantly higher in the RM group than the in-office follow-up group (93.5% vs. 88.7%, P < 0.001).
Actionability of trimonthly scheduled checks was low (6.6%) in both study groups. Overall, actionable causes were reprogramming (76.2%), medication changes (24.8%), and lead/system revisions (4%), and these were not different between the 2 study groups.
The overall mean number of in-clinic and hospital visits was significantly lower in the RM group than the in-office follow-up group (2.1 per patient-year vs. 3.8 per patient-year, P < 0.001), representing a 45% visit reduction at 12 months.
The median time from onset of first arrhythmia to physician evaluation was significantly shorter (P < 0.001) in the RM group than in the in-office follow-up group for all arrhythmias (1 day vs. 35.5 days).
The median time to detect clinically asymptomatic arrhythmia events—atrial fibrillation (AF), ventricular fibrillation (VF), ventricular tachycardia (VT), and supra-ventricular tachycardia (SVT)—was also significantly shorter (P < 0.001) in the RM group compared to the in-office follow-up group (1 day vs. 41.5 days) and was significantly quicker for each of the clinical arrhythmia events—AF (5.5 days vs. 40 days), VT (1 day vs. 28 days), VF (1 day vs. 36 days), and SVT (2 days vs. 39 days).
System-related problems occurred infrequently in both groups—in 1.5% of patients (14/908) in the RM group and in 0.7% of patients (3/432) in the in-office follow-up group.
The overall adverse event rate over 12 months was not significantly different between the 2 groups and individual adverse events were also not significantly different between the RM group and the in-office follow-up group: death (3.4% vs. 4.9%), stroke (0.3% vs. 1.2%), and surgical intervention (6.6% vs. 4.9%), respectively.
The 12-month cumulative survival was 96.4% (95% confidence interval [CI], 95.5%–97.6%) in the RM group and 94.2% (95% confidence interval [CI], 91.8%–96.6%) in the in-office follow-up group, and was not significantly different between the 2 groups (P = 0.174).
The CONNECT Trial
The CONNECT trial, another major multicenter RCT, involved the Care Link® RMS for ICD/CRT devices in a15-month follow-up study of 1,997 patients at 133 sites in the United States. The primary objective of the trial was to determine whether automatically transmitted physician alerts decreased the time from the occurrence of clinically relevant events to medical decisions. The trial results are summarized below:
Of the 575 clinical alerts sent in the study, 246 did not trigger an automatic physician alert. Transmission failures were related to technical issues such as the alert not being programmed or not being reset, and/or a variety of patient factors such as not being at home and the monitor not being plugged in or set up.
The overall mean time from the clinically relevant event to the clinical decision was significantly shorter (P < 0.001) by 17.4 days in the remote follow-up group (4.6 days for 172 patients) than the in-office follow-up group (22 days for 145 patients).
– The median time to a clinical decision was shorter in the remote follow-up group than in the in-office follow-up group for an AT/AF burden greater than or equal to 12 hours (3 days vs. 24 days) and a fast VF rate greater than or equal to 120 beats per minute (4 days vs. 23 days).
Although infrequent, similar low numbers of events involving low battery and VF detection/therapy turned off were noted in both groups. More alerts, however, were noted for out-of-range lead impedance in the RM group (18 vs. 6 patients), and the time to detect these critical events was significantly shorter in the RM group (same day vs. 17 days).
Total in-office clinic visits were reduced by 38% from 6.27 visits per patient-year in the in-office follow-up group to 3.29 visits per patient-year in the remote follow-up group.
Health care utilization visits (N = 6,227) that included cardiovascular-related hospitalization, emergency department visits, and unscheduled clinic visits were not significantly higher in the remote follow-up group.
The overall mean length of hospitalization was significantly shorter (P = 0.002) for those in the remote follow-up group (3.3 days vs. 4.0 days) and was shorter both for patients with ICD (3.0 days vs. 3.6 days) and CRT (3.8 days vs. 4.7 days) implants.
The mortality rate between the study arms was not significantly different between the follow-up groups for the ICDs (P = 0.31) or the CRT devices with defribillator (P = 0.46).
Conclusions
There is limited clinical trial information on the effectiveness of RMSs for PMs. However, for RMSs for ICD devices, multiple cohort studies and 2 large multicenter RCTs demonstrated feasibility and significant reductions in in-office clinic follow-ups with RMSs in the first year post implantation. The detection rates of clinically significant events (and asymptomatic events) were higher, and the time to a clinical decision for these events was significantly shorter, in the remote follow-up groups than in the in-office follow-up groups. The earlier detection of clinical events in the remote follow-up groups, however, was not associated with lower morbidity or mortality rates in the 1-year follow-up. The substitution of almost all the first year in-office clinic follow-ups with RM was also not associated with an increased health care utilization such as emergency department visits or hospitalizations.
The follow-up in the trials was generally short-term, up to 1 year, and was a more limited assessment of potential longer term device/lead integrity complications or issues. None of the studies compared the different RMSs, particularly the different RMSs involving patient-scheduled transmissions or automatic transmissions. Patients’ acceptance of and satisfaction with RM were reported to be high, but the impact of RM on patients’ health-related quality of life, particularly the psychological aspects, was not evaluated thoroughly. Patients who are not technologically competent, having hearing or other physical/mental impairments, were identified as potentially disadvantaged with remote surveillance. Cohort studies consistently identified subgroups of patients who preferred in-office follow-up. The evaluation of costs and workflow impact to the health care system were evaluated in European or American clinical settings, and only in a limited way.
Internet-based device-assisted RMSs involve a new approach to monitoring patients, their disease progression, and their CIEDs. Remote monitoring also has the potential to improve the current postmarket surveillance systems of evolving CIEDs and their ongoing hardware and software modifications. At this point, however, there is insufficient information to evaluate the overall impact to the health care system, although the time saving and convenience to patients and physicians associated with a substitution of in-office follow-up by RM is more certain. The broader issues surrounding infrastructure, impacts on existing clinical care systems, and regulatory concerns need to be considered for the implementation of Internet-based RMSs in jurisdictions involving different clinical practices.
PMCID: PMC3377571  PMID: 23074419
13.  An Epidemiological Network Model for Disease Outbreak Detection 
PLoS Medicine  2007;4(6):e210.
Background
Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most.
Methods and Findings
To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach.
Conclusions
The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.
Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts.
Editors' Summary
Background.
The main task of public-health officials is to promote health in communities around the world. To do this, they need to monitor human health continually, so that any outbreaks (epidemics) of infectious diseases (particularly global epidemics or pandemics) or any bioterrorist attacks can be detected and dealt with quickly. In recent years, advanced disease-surveillance systems have been introduced that analyze data on hospital visits, purchases of drugs, and the use of laboratory tests to look for tell-tale signs of disease outbreaks. These surveillance systems work by comparing current data on the use of health-care resources with historical data or by identifying sudden increases in the use of these resources. So, for example, more doctors asking for tests for salmonella than in the past might presage an outbreak of food poisoning, and a sudden rise in people buying over-the-counter flu remedies might indicate the start of an influenza pandemic.
Why Was This Study Done?
Existing disease-surveillance systems don't always detect disease outbreaks, particularly in situations where there are shifts in the baseline patterns of health-care use. For example, during an epidemic, people might stay away from hospitals because of the fear of becoming infected, whereas after a suspected bioterrorist attack with an infectious agent, hospitals might be flooded with “worried well” (healthy people who think they have been exposed to the agent). Baseline shifts like these might prevent the detection of increased illness caused by the epidemic or the bioterrorist attack. Localized population surges associated with major public events (for example, the Olympics) are also likely to reduce the ability of existing surveillance systems to detect infectious disease outbreaks. In this study, the researchers developed a new class of surveillance systems called “epidemiological network models.” These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams).
What Did the Researchers Do and Find?
The researchers used data collected over a 3-y period from five Boston hospitals on visits for respiratory (breathing) problems and for gastrointestinal (stomach and gut) problems, and on total visits (15 data streams in total), to construct a network model that included all the possible pair-wise comparisons between the data streams. They tested this model by comparing its ability to detect simulated disease outbreaks implanted into data collected over an additional year with that of a reference model based on individual data streams. The network approach, they report, was better at detecting localized outbreaks of respiratory and gastrointestinal disease than the reference approach. To investigate how well the network model dealt with baseline shifts in the use of health-care resources, the researchers then added in a large population surge. The detection performance of the reference model decreased in this test, but the performance of the complete network model and of models that included relationships between only some of the data streams remained stable. Finally, the researchers tested what would happen in a situation where there were large numbers of “worried well.” Again, the network models detected disease outbreaks consistently better than the reference model.
What Do These Findings Mean?
These findings suggest that epidemiological network systems that monitor the relationships between health-care resource-utilization data streams might detect disease outbreaks better than current systems under normal conditions and might be less affected by unpredictable shifts in the baseline data. However, because the tests of the new class of surveillance system reported here used simulated infectious disease outbreaks and baseline shifts, the network models may behave differently in real-life situations or if built using data from other hospitals. Nevertheless, these findings strongly suggest that public-health officials, provided they have sufficient computer power at their disposal, might improve their ability to detect disease outbreaks by using epidemiological network systems alongside their current disease-surveillance systems.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040210.
Wikipedia pages on public health (note that Wikipedia is a free online encyclopedia that anyone can edit, and is available in several languages)
A brief description from the World Health Organization of public-health surveillance (in English, French, Spanish, Russian, Arabic, and Chinese)
A detailed report from the US Centers for Disease Control and Prevention called “Framework for Evaluating Public Health Surveillance Systems for the Early Detection of Outbreaks”
The International Society for Disease Surveillance Web site
doi:10.1371/journal.pmed.0040210
PMCID: PMC1896205  PMID: 17593895
14.  Long-Term Risk of Incident Type 2 Diabetes and Measures of Overall and Regional Obesity: The EPIC-InterAct Case-Cohort Study 
PLoS Medicine  2012;9(6):e1001230.
A collaborative re-analysis of data from the InterAct case-control study conducted by Claudia Langenberg and colleagues has established that waist circumference is associated with risk of type 2 diabetes, independently of body mass index.
Background
Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI).
Methods and Findings
The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5–22.4 kg/m2) with a low WC (<94/80 cm in men/women), the hazard ratio of T2D was 22.0 (95% confidence interval 14.3; 33.8) in men and 31.8 (25.2; 40.2) in women with grade 2 obesity (BMI≥35 kg/m2) and a high WC (>102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50–103 per 1,000 person-years in men and 28–74 per 1,000 person-years in women).
Conclusions
WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
A high body mass index (BMI, a measure of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is a strong predictor of type 2 diabetes. Although the risk of diabetes is greatest in obese people (who have a BMI of greater than 30 kg/m2), many of the people who develop diabetes are overweight—they have a BMI of 25–30 kg/m2. Healthy eating and exercise reduce the incidence of diabetes in high-risk individuals, but it is difficult and expensive to provide all overweight and obese people with individual lifestyle advice. Ideally, a way is needed to distinguish between people with high and low risk of developing diabetes at different levels of BMI. Waist circumference is a measure of fat distribution that has the potential to quantify diabetes risk among people with different BMIs because it estimates the amount of fat around the abdominal organs, which also predicts diabetes development. In this case-cohort study, the researchers use data from the InterAct study (which is investigating how genetics and lifestyle interact to affect diabetes risk) to estimate the long-term risk of type 2 diabetes associated with BMI and waist circumference. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease and in a randomly chosen subcohort.
What Did the Researchers Do and Find?
The researchers estimated the association of BMI and waist circumference with type 2 diabetes from baseline measurements of the weight, height, and waist circumference of 12,403 people who subsequently developed type 2 diabetes and a subcohort of 16,154 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC). Both risk factors were independently associated with type 2 diabetes risk, but waist circumference was a stronger risk factor in women than in men. Obese men (BMI greater than 35 kg/m2) with a high waist circumference (greater than 102 cm) were 22 times more likely to develop diabetes than men with a low normal weight (BMI 18.5–22.4 kg/m2) and a low waist circumference (less than 94 cm); obese women with a waist circumference of more than 88 cm were 31.8 times more likely to develop type 2 diabetes than women with a low normal weight and waist circumference (less than 80 cm). Importantly, among overweight people, waist circumference measurements identified a subgroup of overweight people (those with a high waist circumference) whose 10-year cumulative incidence of type 2 diabetes was similar to that of obese people.
What Do These Findings Mean?
These findings indicate that, among people of European descent, waist circumference is independently and strongly associated with type 2 diabetes, particularly among women. Additional studies are needed to confirm this association in other ethnic groups. Targeted measurement of waist circumference in overweight individuals (who now account for a third of the US and UK adult population) could be an effective strategy for the prevention of diabetes because it would allow the identification of a high-risk subgroup of people who might benefit from individualized lifestyle advice.
Additional Information
Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001230.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health care professionals, and the general public, including detailed information on diabetes prevention (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (including some information in Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes, about the prevention of type 2 diabetes, and about obesity; it also includes peoples stories about diabetes and about obesity
The charity Diabetes UK also provides detailed information for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the charity Healthtalkonline has interviews with people about their experiences of diabetes
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention and about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001230
PMCID: PMC3367997  PMID: 22679397
15.  Identification and Assessment of Public Health Surveillance Gaps under the IHR (2005) 
Objective
To conceive and develop a model to identify gaps in public health surveillance performance and provide a toolset to assess interventions, cost, and return on investment (ROI).
Introduction
Under the revised International Health Regulations (IHR [2005]) one of the eight core capacities is public health surveillance. In May 2012, despite a concerted effort by the global community, the World Health Organization (WHO) reported out that a significant number of member states would not achieve targeted capacity in the IHR (2005) surveillance core capacity.
Currently, there is no model to identify and measure these gaps in surveillance performance. Likewise, there is no toolset to assess interventions by cost and estimate the ROI.
We developed a new conceptual framework that: (1) described the work practices to achieve effective and efficient public health surveillance; (2) could identify impediments or gaps in performance; and (3) will assist program managers in decision making.
Methods
Published articles and grey-literature reports, manuals and logic model examples were gathered through a literature review of PubMed, Web of Science, Google Scholar, and other databases. Logic models were conceived by categorizing discrete surveillance inputs, activities, outputs, and outcomes. Indicators were selected from authoritative sources or developed and then mapped to the logic model elements. These indicators will be weighted using the principle component analysis (PCA), a method for enhanced precision of statistical analysis. Finally, on the front end of the tool, indicators will graphically measure the surveillance gap expressed through the tool’s architecture and provide information using an integrated cost-impact analysis.
Results
We developed five public health surveillance logic models: for IHR (2005) compliance; event-based; indicator-based; syndromic; and predictive surveillance domains. The IHR (2005) domain focused on national-level functionality, and the others described the complexities of their specific surveillance work practices. Indicators were then mapped and linked to all logic model elements.
Conclusions
This new framework, intended for self-administration at the national and subnational levels, measured public health surveillance gaps in performance and provided cost and ROI information by intervention. The logic model framework and PCA methodology are tools that both describe work processes and define appropriate variables used for evaluation. However, both require real-world data. We recommend pilot testing and validation of this new framework. Once piloted, the framework could be adapted for the other IHR (2005) core capacities.
PMCID: PMC3692929
Public health surveillance; Evaluation; IHR (2005); Gaps assessment; Cost-impact analysis
16.  Surveillance for Radiation-Related Exposures Reported to the National Poison Data System 
Objective
To describe radiation-related exposures of potential public health significance reported to the National Poison Data System (NPDS).
Introduction
For radiological incidents, collecting surveillance data can identify radiation-related public health significant incidents quickly and enable public health officials to describe the characteristics of the affected population and the magnitude of the health impact which in turn can inform public health decision-making. A survey administered by the Council of State and Territorial Epidemiologists (CSTE) to state health departments in 2010 assessed the extent of state-level planning for surveillance of radiation-related exposures and incidents: 70%–84% of states reported minimal or no planning completed. One data source for surveillance of radiological exposures and illnesses is regional poison centers (PCs), who receive information requests and reported exposures from healthcare providers and the public. Since 2010, the Centers for Disease Control and Prevention (CDC) and the American Association of Poison Control Centers (AAPCC) have conducted ongoing surveillance for exposures to radiation and radioactive materials reported from all 57 United States (US) PCs to NPDS, a web-based, national PC reporting database and surveillance system.
Methods
We collaborated with the American Association of Poison Control Centers (AAPCC), Poisindex® and Thomson Reuters Healthcare to develop an improved coding system for tracking radiation-related exposures reported to US PCs during 2011 and trained PC staff on its usage. We reviewed NPDS data from 1 September 2010 – 30 June 2012 for reported exposures to pharmaceutical or nonpharmaceutical radionuclides; ionizing radiation; radiological or nuclear weapons; or X-ray, alpha, beta, gamma, or neutron radiation. CDC medical toxicology and epidemiology staff reviewed each reported exposure to determine whether it was of potential public health concern (e.g. exposures associated with an ongoing public health emergency, several reported exposures clustered in space and time). When further information was needed to classify the potential public health importance of a call, CDC and AAPCC staff contacted the regional PC where each call originated. When exposures were spatially and temporally clustered, we reviewed news stories in the public media for evidence of an associated radiation incident.
Results
Of 419 exposures reported during the study period, 25 were associated with a radiation-related incident. Of these, 4 were related to an exposure to x-ray radiation from an industrial radiography incident, 11 were related to a transportation accident involving potential contamination with radioactive material, and 10 were related to the Fukushima Daiichi Japan nuclear reactor disaster. Public health, hazardous materials, or hospital radiation safety staff were involved in responding to each of these events. We also identified 26 reported exposures associated with a regional radiation anti-terrorism exercise. The reported exposures were followed-up and removed from analysis once we determined they were part of the exercise. The remaining (n=368; 88%) were either requests for information, confirmed non-exposures, or exposures deemed unrelated or non-significant.
Conclusions
The capability to monitor self- or clinician-reported exposures to radiation and radioactive materials is available in NPDS for state and local public health use in collaboration with their regional PC and may improve public health capacity to identify and respond to radiological emergencies. Next steps include testing the system’s capability to accurately classify and rapidly respond to a cluster of calls to PCs reporting radiation exposures associated with a “dirty bomb” exercise during July, 2012.
PMCID: PMC3692946
Surveillance; Poison center; radiation
17.  Operational Experience: Integration of ASPR Data into ESSENCE-FL during the RNC 
Objective
The Florida Department of Health (FDOH), Bureau of Epidemiology, partnered with the U.S. Department of Health and Human Services (HHS) Office of the Assistant Secretary for Preparedness and Response (ASPR) to improve surveillance methods in post disaster or response events. A new process was implemented for conducting surveillance to monitor injury and illness for those presenting for care to ASPR assets such as Disaster Medical Assistance Team (DMAT) sites when they are operational in the state. The purpose of the current work was to field test and document the operational experience of the newly implemented ASPR data module in ESSENCE-FL (syndromic surveillance system) to receive near real-time automated data feeds when ASPR federal assets were deployed in Florida during the 2012 Republican National Convention (RNC).
Introduction
Florida has implemented various surveillance methods to augment existing sources of surveillance data and enhance decision making with timely evidence based assessments to guide response efforts post-hurricanes. Historically, data collected from deployed federal assets have been an integral part of this effort. However, a number of factors have made this type of surveillance challenging: logistical issues of field work in a post-disaster environment, the resource intensive manual data collection process from DMAT sites, and delayed analysis and interpretation of these data to inform decision makers. The ESSENCE-FL system is an automated and secure web-based application accessed by FDOH epidemiologists and staff at participating hospitals.
Methods
ESSENCE-FL was configured by the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to receive ASPR electronic medical record (EMR) data. A scheduled program to generate data files for FDOH was created using SAS Enterprise Business Intelligence (EBI) software and a script was set up on the ASPR server to send an updated file via secure file transfer protocol (sftp) every 15 minutes. A case definition was created by ASPR field teams to identify which encounter visits would be entered into the electronic medical record (EMR) and received in ESSENCE-FL. To assess completeness of data elements and total patient encounters received in ESSENCE-FL, DMAT field teams maintained Excel line lists of patient encounters and emailed them to FDOH three times daily during the RNC. ASPR data were reviewed and analyzed by FDOH staff multiple times a day in near real time utilizing the existing ESSENCE-FL robust analysis tools.
Results
Three separate ASPR missions were deployed to Florida to support the RNC. ASPR EMR data files were received at 15-minute intervals by ESSENCE-FL from the ASPR central server during each day of the 2012 RNC (August 26–31). Reduced patient counts within ESSENCE-FL as compared with DMAT-maintained Excel line lists indicated an incomplete input, upload, or transfer of patient data from one of two ASPR sites to the central ASPR servers. Although only 11 of 34 total patient encounters were received by ESSENCE-FL during the event, the system design enabled users to run specific queries and display the results of their queries in time series graphs, pie and bar charts, GIS maps, dashboards, and statistical tables.
Conclusions
There is a great need to have timely access to data sources to enhance disease surveillance efforts and help guide decision makers’ situational awareness and disease control efforts during a response. The FDOH, Bureau of Epidemiology’s collaboration with JHU/APL and ASPR takes advantage of ASPR’s EMR-S to make data sharing and analysis efficient as evidenced during the RNC. Automated data feeds to ESSENCE-FL removed resource intensive manual data collection by public health, improved standardization of syndrome and demographic categorizations, increased access to these data by local, state, and federal epidemiologists in a timely manner, and expedited analysis and interpretation for situational awareness. Future recommendations include pre-event testing of the entire data flow process, establishing an on-site specialist to immediately assist with any issues, greater understanding of the field team use of the EMR-S, and ensuring field staff is aware of data quality needs for effective public health surveillance.
PMCID: PMC3692919
surveillance; response; disaster
18.  Mendelian Randomization Study of B-Type Natriuretic Peptide and Type 2 Diabetes: Evidence of Causal Association from Population Studies 
PLoS Medicine  2011;8(10):e1001112.
Using mendelian randomization, Roman Pfister and colleagues demonstrate a potentially causal link between low levels of B-type natriuretic peptide (BNP), a hormone released by damaged hearts, and the development of type 2 diabetes.
Background
Genetic and epidemiological evidence suggests an inverse association between B-type natriuretic peptide (BNP) levels in blood and risk of type 2 diabetes (T2D), but the prospective association of BNP with T2D is uncertain, and it is unclear whether the association is confounded.
Methods and Findings
We analysed the association between levels of the N-terminal fragment of pro-BNP (NT-pro-BNP) in blood and risk of incident T2D in a prospective case-cohort study and genotyped the variant rs198389 within the BNP locus in three T2D case-control studies. We combined our results with existing data in a meta-analysis of 11 case-control studies. Using a Mendelian randomization approach, we compared the observed association between rs198389 and T2D to that expected from the NT-pro-BNP level to T2D association and the NT-pro-BNP difference per C allele of rs198389. In participants of our case-cohort study who were free of T2D and cardiovascular disease at baseline, we observed a 21% (95% CI 3%–36%) decreased risk of incident T2D per one standard deviation (SD) higher log-transformed NT-pro-BNP levels in analysis adjusted for age, sex, body mass index, systolic blood pressure, smoking, family history of T2D, history of hypertension, and levels of triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The association between rs198389 and T2D observed in case-control studies (odds ratio = 0.94 per C allele, 95% CI 0.91–0.97) was similar to that expected (0.96, 0.93–0.98) based on the pooled estimate for the log-NT-pro-BNP level to T2D association derived from a meta-analysis of our study and published data (hazard ratio = 0.82 per SD, 0.74–0.90) and the difference in NT-pro-BNP levels (0.22 SD, 0.15–0.29) per C allele of rs198389. No significant associations were observed between the rs198389 genotype and potential confounders.
Conclusions
Our results provide evidence for a potential causal role of the BNP system in the aetiology of T2D. Further studies are needed to investigate the mechanisms underlying this association and possibilities for preventive interventions.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, nearly 250 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous amounts of sugar (glucose) in the blood. Blood sugar levels are normally controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the most common form of diabetes), blood sugar control fails because the fat and muscle cells that usually respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include kidney failure and an increased risk of cardiovascular problems such as heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
Because the causes of type 2 diabetes are poorly understood, it is hard to devise ways to prevent the condition. Recently, B-type natriuretic peptide (BNP, a hormone released by damaged hearts) has been implicated in type 2 diabetes development in cross-sectional studies (investigations in which data are collected at a single time point from a population to look for associations between an illness and potential risk factors). Although these studies suggest that high levels of BNP may protect against type 2 diabetes, they cannot prove a causal link between BNP levels and diabetes because the study participants with low BNP levels may share some another unknown factor (a confounding factor) that is the real cause of both diabetes and altered BNP levels. Here, the researchers use an approach called “Mendelian randomization” to examine whether reduced BNP levels contribute to causing type 2 diabetes. It is known that a common genetic variant (rs198389) within the genome region that encodes BNP is associated with a reduced risk of type 2 diabetes. Because gene variants are inherited randomly, they are not subject to confounding. So, by investigating the association between BNP gene variants that alter NT-pro-BNP (a molecule created when BNP is being produced) levels and the development of type 2 diabetes, the researchers can discover whether BNP is causally involved in this chronic condition.
What Did the Researchers Do and Find?
The researchers analyzed the association between blood levels of NT-pro-BNP at baseline in 440 participants of the EPIC-Norfolk study (a prospective population-based study of lifestyle factors and the risk of chronic diseases) who subsequently developed diabetes and in 740 participants who did not develop diabetes. In this prospective case-cohort study, the risk of developing type 2 diabetes was associated with lower NT-pro-BNP levels. They also genotyped (sequenced) rs198389 in the participants of three case-control studies of type 2 diabetes (studies in which potential risk factors for type 2 diabetes were examined in people with type 2 diabetes and matched controls living in the East of England), and combined these results with those of eight similar published case-control studies. Finally, the researchers showed that the association between rs198389 and type 2 diabetes measured in the case-control studies was similar to the expected association calculated from the association between NT-pro-BNP level and type 2 diabetes obtained from the prospective case-cohort study and the association between rs198389 and BNP levels obtained from the EPIC-Norfolk study and other published studies.
What Do These Findings Mean?
The results of this Mendelian randomization study provide evidence for a causal, protective role of the BNP hormone system in the development of type 2 diabetes. That is, these findings suggest that low levels of BNP are partly responsible for the development of type 2 diabetes. Because the participants in all the individual studies included in this analysis were of European descent, these findings may not be generalizable to other ethnicities. Moreover, they provide no explanation of how alterations in the BNP hormone system might affect the development of type 2 diabetes. Nevertheless, the demonstration of a causal link between the BNP hormone system and type 2 diabetes suggests that BNP may be a potential target for interventions designed to prevent type 2 diabetes, particularly since the feasibility of altering BNP levels with drugs has already been proven in patients with cardiovascular disease.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001112.
The International Diabetes Federation provides information about all aspects of diabetes
The US National Diabetes Information Clearinghouse provides detailed information about diabetes for patients, health-care professionals, and the general public (in English and Spanish)
The UK National Health Service Choices website also provides information for patients and carers about type 2 diabetes and includes people's stories about diabetes
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
Wikipedia has pages on BNP and on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The charity Healthtalkonline has interviews with people about their experiences of diabetes; the charity Diabetes UK has a further selection of stories from people with diabetes
doi:10.1371/journal.pmed.1001112
PMCID: PMC3201934  PMID: 22039354
19.  Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance 
Background
A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics.
Methodology/Principal Findings
Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003–2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99.
Conclusions/Significance
Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.
Author Summary
A variety of obstacles, including bureaucracy and lack of resources, delay detection and reporting of dengue and exist in many countries where the disease is a major public health threat. Surveillance efforts have turned to modern data sources such as Internet usage data. People often seek health-related information online and it has been found that the frequency of, for example, influenza-related web searches as a whole rises as the number of people sick with influenza rises. Tools have been developed to help track influenza epidemics by finding patterns in certain web search activity. However, few have evaluated whether this approach would also be effective for other diseases, especially those that affect many people, that have severe consequences, or for which there is no vaccine. In this study, we found that aggregated, anonymized Google search query data were also capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after a long delay, web search query data is available for analysis within a day. Therefore, because it could potentially provide earlier warnings, these data represent a valuable complement to traditional dengue surveillance.
doi:10.1371/journal.pntd.0001206
PMCID: PMC3104029  PMID: 21647308
20.  The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors 
PLoS Medicine  2009;6(4):e1000058.
Majid Ezzati and colleagues examine US data on risk factor exposures and disease-specific mortality and find that smoking and hypertension, which both have effective interventions, are responsible for the largest number of deaths.
Background
Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight–obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking.
Methods and Findings
We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000–500,000) and 395,000 (372,000–414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight–obesity (216,000; 188,000–237,000) and physical inactivity (191,000; 164,000–222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000–107,000), low dietary omega-3 fatty acids (84,000; 72,000–96,000), and high dietary trans fatty acids (82,000; 63,000–97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000–40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000–94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis, alcohol use disorders, road traffic and other injuries, and violence.
Conclusions
Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.
Please see later in the article for Editors' Summary
Editors' Summary
Background
A number of modifiable factors are responsible for many premature or preventable deaths. For example, being overweight or obese shortens life expectancy, while half of all long-term tobacco smokers in Western populations will die prematurely from a disease directly related to smoking. Modifiable risk factors fall into three main groups. First, there are lifestyle risk factors. These include tobacco smoking, physical inactivity, and excessive alcohol use (small amounts of alcohol may actually prevent diabetes and some types of heart disease and stroke). Second, there are dietary risk factors such as a high salt intake and a low intake of fruits and vegetables. Finally, there are “metabolic risk factors,” which shorten life expectancy by increasing a person's chances of developing cardiovascular disease (in particular, heart problems and strokes) and diabetes. Metabolic risk factors include having high blood pressure or blood cholesterol and being overweight or obese.
Why Was This Study Done?
It should be possible to reduce preventable deaths by changing modifiable risk factors through introducing public health policies, programs and regulations that reduce exposures to these risk factors. However, it is important to know how many deaths are caused by each risk factor before developing policies and programs that aim to improve a nation's health. Although previous studies have provided some information on the numbers of premature deaths caused by modifiable risk factors, there are two problems with these studies. First, they have not used consistent and comparable methods to estimate the number of deaths attributable to different risk factors. Second, they have rarely considered the effects of dietary and metabolic risk factors. In this new study, the researchers estimate the number of deaths due to 12 different modifiable dietary, lifestyle, and metabolic risk factors for the United States population. They use a method called “comparative risk assessment.” This approach estimates the number of deaths that would be prevented if current distributions of risk factor exposures were changed to hypothetical optimal distributions.
What Did the Researchers Do and Find?
The researchers extracted data on exposures to these 12 selected risk factors from US national health surveys, and they obtained information on deaths from difference diseases for 2005 from the US National Center for Health Statistics. They used previously published studies to estimate how much each risk factor increases the risk of death from each disease. The researchers then used a mathematical formula to estimate the numbers of deaths caused by each risk factor. Of the 2.5 million US deaths in 2005, they estimate that nearly half a million were associated with tobacco smoking and about 400,000 were associated with high blood pressure. These two risk factors therefore each accounted for about 1 in 5 deaths in US adults. Overweight–obesity and physical inactivity were each responsible for nearly 1 in 10 deaths. Among the dietary factors examined, high dietary salt intake had the largest effect, being responsible for 4% of deaths in adults. Finally, while alcohol use prevented 26,000 deaths from ischemic heart disease, ischemic stroke, and diabetes, the researchers estimate that it caused 90,000 deaths from other types of cardiovascular diseases, other medical conditions, and road traffic accidents and violence.
What Do These Findings Mean?
These findings indicate that smoking and high blood pressure are responsible for the largest number of preventable deaths in the US, but that several other modifiable risk factors also cause many deaths. Although the accuracy of some of the estimates obtained in this study will be affected by the quality of the data used, these findings suggest that targeting a handful of risk factors could greatly reduce premature mortality in the US. The findings might also apply to other countries, although the risk factors responsible for most preventable deaths may vary between countries. Importantly, effective individual-level and population-wide interventions are already available to reduce people's exposure to the two risk factors responsible for most preventable deaths in the US. The researchers also suggest that combinations of regulation, pricing, and education have the potential to reduce the exposure of US residents to other risk factors that are likely to shorten their lives.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000058.
The MedlinePlus encyclopedia contains a page on healthy living (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
Healthy People 2010 is a national framework designed to improve the health of people living in the US. The Healthy People 2020 Framework is due to be launched in January 2010
The World Health Report 2002Reducing Risks, Promoting Healthy Life provides a global analysis of how healthy life expectancy could be increased
The National Health and Nutrition Examination Survey (NHANES) is “a program of studies designed to assess the health and nutritional status of adults and children in the United States”
The US Centers for Disease Control and Prevention's site Smoking and Tobacco Use offers a large number of informational and data resources on this important risk factor
The American Heart Association and American Cancer Society provide a rich resource for patients and caregivers on many important risk factors including diet, sodium intake, and smoking
doi:10.1371/journal.pmed.1000058
PMCID: PMC2667673  PMID: 19399161
21.  Muscle-Strengthening and Conditioning Activities and Risk of Type 2 Diabetes: A Prospective Study in Two Cohorts of US Women 
PLoS Medicine  2014;11(1):e1001587.
Anders Grøntved and colleagues examined whether women who perform muscle-strengthening and conditioning activities have an associated reduced risk of type 2 diabetes mellitus.
Please see later in the article for the Editors' Summary
Background
It is well established that aerobic physical activity can lower the risk of type 2 diabetes (T2D), but whether muscle-strengthening activities are beneficial for the prevention of T2D is unclear. This study examined the association of muscle-strengthening activities with the risk of T2D in women.
Methods and Findings
We prospectively followed up 99,316 middle-aged and older women for 8 years from the Nurses' Health Study ([NHS] aged 53–81 years, 2000–2008) and Nurses' Health Study II ([NHSII] aged 36–55 years, 2001–2009), who were free of diabetes, cancer, and cardiovascular diseases at baseline. Participants reported weekly time spent on resistance exercise, lower intensity muscular conditioning exercises (yoga, stretching, toning), and aerobic moderate and vigorous physical activity (MVPA) at baseline and in 2004/2005. Cox regression with adjustment for major determinants for T2D was carried out to examine the influence of these types of activities on T2D risk. During 705,869 person years of follow-up, 3,491 incident T2D cases were documented. In multivariable adjusted models including aerobic MVPA, the pooled relative risk (RR) for T2D for women performing 1–29, 30–59, 60–150, and >150 min/week of total muscle-strengthening and conditioning activities was 0.83, 0.93, 0.75, and 0.60 compared to women reporting no muscle-strengthening and conditioning activities (p<0.001 for trend). Furthermore, resistance exercise and lower intensity muscular conditioning exercises were each independently associated with lower risk of T2D in pooled analyses. Women who engaged in at least 150 min/week of aerobic MVPA and at least 60 min/week of muscle-strengthening activities had substantial risk reduction compared with inactive women (pooled RR = 0.33 [95% CI 0.29–0.38]). Limitations to the study include that muscle-strengthening and conditioning activity and other types of physical activity were assessed by a self-administered questionnaire and that the study population consisted of registered nurses with mostly European ancestry.
Conclusions
Our study suggests that engagement in muscle-strengthening and conditioning activities (resistance exercise, yoga, stretching, toning) is associated with a lower risk of T2D. Engagement in both aerobic MVPA and muscle-strengthening type activity is associated with a substantial reduction in the risk of T2D in middle-aged and older women.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 370 million people have diabetes mellitus, a disorder characterized by poor glycemic control—dangerously high amounts of glucose (sugar) in the blood. Blood sugar levels are normally controlled by insulin, a hormone released by the pancreas. In people with type 2 diabetes (the commonest form of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes, which was previously known as adult-onset diabetes, can often initially be controlled with diet and exercise, and with antidiabetic drugs such as metformin and sulfonylureas. However, as the disease progresses, the pancreatic beta cells, which make insulin, become impaired and patients may eventually need insulin injections. Long-term complications of diabetes, which include an increased risk of cardiovascular problems such as heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is becoming increasingly common worldwide so better preventative strategies are essential. It is well-established that regular aerobic exercise—physical activity in which the breathing and heart rate increase noticeably such as jogging, brisk walking, and swimming—lowers the risk of type 2 diabetes. The World Health Organization currently recommends that adults should do at least 150 min/week of moderate-to-vigorous aerobic physical activity to reduce the risk of diabetes and other non-communicable diseases. It also recommends that adults should undertake muscle-strengthening and conditioning activities such as weight training and yoga on two or more days a week. However, although studies have shown that muscle-strengthening activity improves glycemic control in people who already have diabetes, it is unclear whether this form of exercise prevents diabetes. In this prospective cohort study (a study in which disease development is followed up over time in a group of people whose characteristics are recorded at baseline), the researchers investigated the association of muscle-strengthening activities with the risk of type 2 diabetes in women.
What Did the Researchers Do and Find?
The researchers followed up nearly 100,000 women enrolled in the Nurses' Health Study (NHS) and the Nurses' Health Study II (NHSII), two prospective US investigations into risk factors for chronic diseases in women, for 8 years. The women provided information on weekly participation in muscle-strengthening exercise (for example, weight training), lower intensity muscle-conditioning exercises (for example, yoga and toning), and aerobic moderate and vigorous physical activity (aerobic MVPA) at baseline and 4 years later. During the study 3,491 women developed diabetes. After allowing for major risk factors for type 2 diabetes (for example, diet and a family history of diabetes) and for aerobic MVPA, compared to women who did no muscle-strengthening or conditioning exercise, the risk of developing type 2 diabetes among women declined with increasing participation in muscle-strengthening and conditioning activity. Notably, women who did more than 150 min/week of these types of exercise had 40% lower risk of developing diabetes as women who did not exercise in this way at all. Muscle-strengthening and muscle-conditioning exercise were both independently associated with reduced diabetes risk, and women who engaged in at least 150 min/week of aerobic MVPA and at least 60 min/week of muscle-strengthening exercise were a third as likely to develop diabetes as inactive women.
What Do These Findings Mean?
These findings show that, among the women enrolled in NHS and NHSII, engagement in muscle-strengthening and conditioning activities lowered the risk of type 2 diabetes independent of aerobic MVPA. That is, non-aerobic exercise provided protection against diabetes in women who did no aerobic exercise. Importantly, they also show that doing both aerobic exercise and muscle-strengthening exercise substantially reduced the risk of type 2 diabetes. Because nearly all the participants in NHS and NHSII were of European ancestry, these results may not be generalizable to women of other ethnic backgrounds. Moreover, the accuracy of these findings may be limited by the use of self-administered questionnaires to determine how much exercise the women undertook. Nevertheless, these findings support the inclusion of muscle-strengthening and conditioning exercises in strategies designed to prevent type 2 diabetes in women, a conclusion that is consistent with current guidelines for physical activity among adults.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001587.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and explains the benefits of regular physical activity
The World Health Organization provides information about diabetes and about physical activity and health (in several languages); its 2010 Global Recommendations on Physical Activity for Health are available in several languages
The US Centers for Disease Control and Prevention provides information on physical activity for different age groups; its Physical Activity for Everyone web pages include guidelines, instructional videos and personal success stories
More information about the Nurses Health Study and the Nurses Health Study II is available
The UK charity Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes and about physical exercise and fitness (in English and Spanish)
doi:10.1371/journal.pmed.1001587
PMCID: PMC3891575  PMID: 24453948
22.  Preeclampsia as a Risk Factor for Diabetes: A Population-Based Cohort Study 
PLoS Medicine  2013;10(4):e1001425.
Denice Feig and colleagues assess the association between gestational diabetes, gestational hypertension, and preeclampsia and the development of future diabetes in a database analysis of pregnant women in Ontario, Canada.
Background
Women with preeclampsia (PEC) and gestational hypertension (GH) exhibit insulin resistance during pregnancy, independent of obesity and glucose intolerance. Our aim was to determine whether women with PEC or GH during pregnancy have an increased risk of developing diabetes after pregnancy, and whether the presence of PEC/GH in addition to gestational diabetes (GDM) increases the risk of future (postpartum) diabetes.
Methods and Findings
We performed a population-based, retrospective cohort study for 1,010,068 pregnant women who delivered in Ontario, Canada between April 1994 and March 2008. Women were categorized as having PEC alone (n = 22,933), GH alone (n = 27,605), GDM alone (n = 30,852), GDM+PEC (n = 1,476), GDM+GH (n = 2,100), or none of these conditions (n = 925,102). Our main outcome was a new diagnosis of diabetes postpartum in the following years, up until March 2011, based on new records in the Ontario Diabetes Database. The incidence rate of diabetes per 1,000 person-years was 6.47 for women with PEC and 5.26 for GH compared with 2.81 in women with neither of these conditions. In the multivariable analysis, both PEC alone (hazard ratio [HR] = 2.08; 95% CI 1.97–2.19) and GH alone (HR = 1.95; 95% CI 1.83–2.07) were risk factors for subsequent diabetes. Women with GDM alone were at elevated risk of developing diabetes postpartum (HR = 12.77; 95% CI 12.44–13.10); however, the co–presence of PEC or GH in addition to GDM further elevated this risk (HR = 15.75; 95% CI 14.52–17.07, and HR = 18.49; 95% CI 17.12–19.96, respectively). Data on obesity were not available.
Conclusions
Women with PEC/GH have a 2-fold increased risk of developing diabetes when followed up to 16.5 years after pregnancy, even in the absence of GDM. The presence of PEC/GH in the setting of GDM also raised the risk of diabetes significantly beyond that seen with GDM alone. A history of PEC/GH during pregnancy should alert clinicians to the need for preventative counseling and more vigilant screening for diabetes.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin (a hormone that regulates blood sugar), known as type 1 diabetes, or when the body cannot effectively use the insulin it produces—type 2 diabetes. Raised blood sugar, is a common effect of uncontrolled diabetes and over time leads to serious complications and even death. Worryingly, the global burden of type 2 diabetes is increasing worldwide, and the World Health Organization estimates that 90% of the 347 million people with diabetes currently have type 2 diabetes. Previous studies have shown that type 2 diabetes can be prevented or delayed in high risk groups by a range of lifestyle and treatment interventions and so it is important to identify potential high risk groups to screen for type 2 diabetes.
Why Was This Study Done?
Gestational diabetes (a form of diabetes that is related to pregnancy) is a major risk factor for developing type 2 diabetes. Therefore, diabetes prevention strategies should target women with gestational diabetes. Likewise, other common disorders of pregnancy possibly associated with insulin resistance, such as preeclampsia (a condition in which affected women have high blood pressure, fluid retention, and protein in their urine) and gestational hypertension (high blood pressure associated with pregnancy), may lead to the future development of type 2 diabetes. So women with these conditions may also benefit from diabetes prevention strategies. Therefore, in this large database study from Ontario, Canada, the researchers examined whether pregnant women with preeclampsia or gestational hypertension had an increased risk of developing diabetes in the years following pregnancy even if they did not have gestational diabetes.
What Did the Researchers Do and Find?
The researchers used a comprehensive Canadian health database to identify all women age 15 to 50 years of age who delivered in an Ontario hospital between April 1994 and March 2008. They then identified women who had preeclampsia, gestational hypertension, or gestational diabetes through hospital records and outpatient information. The researchers then used records from the Ontario Diabetes Database to record whether these women went on to develop diabetes in the period from 180 days after delivery until March 2011.
Using these methods, the researchers identified 1,010,068 pregnant women suitable for analysis, of whom 22,933 had only preeclampsia, 27,605 had only gestational hypertension, and 30,852 had only gestational diabetes: 2,100 women had both gestational diabetes and gestational hypertension and 1,476 women had gestational diabetes and preeclampsia. Overall, 35,077 women developed diabetes (3.5%) in the follow-up period (median of 8.5 years) at a median age of 37 years. In a modeling analysis, the researchers found that women with gestational diabetes had a 15-fold increased rate of developing diabetes compared to women without gestational diabetes, gestational hypertension, and preeclampsia, while women with gestational diabetes plus either preeclampsia or gestational hypertension had a 20- to 21-fold increased rate. These results were slightly reduced after adjusting for age, income quintile, hypertension prior to pregnancy, and co-morbidity, giving a hazard ratio (HR) of 1.95 for gestational hypertension alone, an HR of 2.08 for preeclampsia alone, an HR of 12.77 for gestational diabetes alone, an HR of 18.49 for gestational diabetes plus gestational hypertension and finally, an HR of 15.75 for gestational diabetes plus preeclampsia.
These Findings Mean?
These findings suggest that both preeclampsia and gestational hypertension without gestational diabetes are associated with a 2-fold increased incidence of diabetes in the years following pregnancy after controlling for several important variables. When combined with gestational diabetes, these conditions were associated with a further elevation in diabetes incidence additional to the 13-fold increased incidence resulting from gestational diabetes alone. A limitation of this study was the lack of information on obesity and body mass index, factors which are also associated with increased risk of developing diabetes. Nevertheless, these findings highlight a possible new risk factor for diabetes, and suggest that clinicians should be aware of the need for preventative measures and vigilant screening for diabetes in women with a history of preeclampsia or gestational hypertension.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001425.
NHS Choices has information about preeclampsia, gestational diabetes, and gestational hypertension
Living with diabetes is a useful resource for patients with diabetes
The Preeclampsia Foundation has more information about preeclampsia
doi:10.1371/journal.pmed.1001425
PMCID: PMC3627640  PMID: 23610560
23.  Enhanced Surveillance during the Democratic National Convention, Charlotte, NC 
Objective
To describe how the existing state syndromic surveillance system (NC DETECT) was enhanced to facilitate surveillance conducted at the Democratic National Convention in Charlotte, North Carolina from August 31, 2012 to September 10, 2012.
Introduction
North Carolina hosted the 2012 Democratic National Convention, September 3–6, 2012. The NC Epidemiology and Surveillance Team was created to facilitate enhanced surveillance for injuries and illnesses, early detection of outbreaks during the DNC, assist local public health with epidemiologic investigations and response, and produce daily surveillance reports for internal and external stakeholders. Surveillane data were collected from several data sources, including North Carolina Electronic Disease Surveillance System (NC EDSS), triage stations, and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).
NC DETECT was created by the North Carolina Division of Public Health (NC DPH) in 2004 in collaboration with the Carolina Center for Health Informatics (CCHI) in the UNC Department of Emergency Medicine to address the need for early event detection and timely public health surveillance in North Carolina using a variety of secondary data sources. The data from emergency departments, the Carolinas Poison Center, the Pre-hospital Medical Information System (PreMIS) and selected Urgent Care Centers were available for monitoring by authorized users during the DNC.
Methods
Within NC DETECT, new dashboards were created that allowed epidemiologists to monitor ED visits and calls to the poison center in the Charlotte area, the greater Cities Readiness Initiative region and the entire state for infectious disease signs and symptoms, injuries and any mention of bioterrorism agents. The dashboards also included a section to view user comments on the information presented in NC DETECT. Data processing changes were also made to improve the timeliness of the EMS data received from PreMIS.
Results
The DNC dashboards added to NC DETECT streamlined the workflow by placing all syndromes and annotations of interest in one place, with the date ranges and locations already pre-selected. Graphs in the dashboards could be easily copied and pasted into situation reports. The prompt development of these user-friendly tools provided effective surveillance for this mass gathering and ensured timely control measures, if necessary.
Conclusions
Syndromic surveillance systems can be enhanced to provide detailed, specific surveillance during mass gathering events. Elements that facilitate this enhancement include strong communication between skilled users and the informatics team, in order to minimize the burden placed on the surveillance team system users, data sources and system developers during the event. The visualizations developed as part of these new dashboards will be leveraged to provide additional tools to other NC DETECT user groups, including hospital-based public health epidemiologists and local health department users.
PMCID: PMC3692843
dashboards; enhanced surveillance; Democratic National Convention
24.  SAGES Update: Electronic Disease Surveillance in Resource-Limited Settings 
Objective
The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, open-source software tools for electronic disease surveillance in resource-limited settings. This demonstration will illustrate several new innovations and update attendees on new users in Africa and Asia.
Introduction
The new 2005 International Health Regulations (IHR), a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance with particular emphasis on resource-limited settings. More than a decade ago, in collaboration with the US Department of Defense (DoD), the Johns Hopkins University Applied Physics Laboratory (JHU/APL) developed the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). ESSENCE collects, processes, and analyzes non-traditional data sources (i.e. chief complaints from hospital emergency departments, school absentee data, poison control center calls, over-the-counter pharmaceutical sales, etc.) to identify anomalous disease activity in a community. The data can be queried, analyzed, and visualized both temporally and spatially by the end user. The current SAGES initiative leverages the experience gained in the development of ESSENCE, and the analysis and visualization components of SAGES are built with the same features in mind.
Methods
SAGES tools are organized into four categories: 1) data collection, 2) analysis & visualization, 3) communications, and 4) modeling/simulation/evaluation. Within each category, SAGES offers a variety of tools compatible with surveillance needs and different types or levels of information technology infrastructure. SAGES tools are built in a modular nature, which allows for the user to select one or more tools to enhance an existing surveillance system or use the tools en masse for an end-to-end electronic disease surveillance capability. Thus, each locality can select tools from SAGES based upon their needs, capabilities, and existing systems to create a customized electronic disease surveillance system. New OpenESSENCE developments include improved data query ability, improved mapping functionality, and enhanced training materials. New cellular phone developments include the ability to concatenate single SMS messages sent by simple or Smart Android cell phones. This ‘multiple-SMS’ message ability allows use of SMS technology to send and receive health information exceeding normal SMS message length in a manner transparent to the users.
Conclusions
The SAGES project is intended to enhance electronic disease surveillance capacity in resource-limited settings around the world. We have combined electronic disease surveillance tools developed at JHU/APL with other freely-available, interoperable software tools to create SAGES. We believe this suite of tools will facilitate local and regional electronic disease surveillance, regional public health collaborations, and international disease reporting. SAGES development, funded by the US Armed Forces Health Surveillance Center, continues as we add new international collaborators. SAGES tools are currently deployed in locations in Africa, Asia and South America, and are offered to other interested countries around the world.
PMCID: PMC3692858
software; surveillance; electronic; open-source
25.  Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study 
PLoS Medicine  2014;11(5):e1001647.
In this study, Wareham and colleagues quantified the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. The authors found that the relative effect of a type 2 diabetes genetic risk score is greater in younger and leaner participants, and the high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Background
Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
Methods and Findings
The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10−4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10−3) and waist circumference (p for interaction  = 7.49×10−9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.
Conclusions
The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 380 million people currently have diabetes, and the condition is becoming increasingly common. Diabetes is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes can often initially be controlled with diet and exercise (lifestyle changes) and with antidiabetic drugs such as metformin and sulfonylureas, but patients may eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about ten years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is thought to originate from the interplay between genetic and lifestyle factors. But although rapid progress is being made in understanding the genetic basis of type 2 diabetes, it is not known whether the consequences of adverse lifestyles (for example, being overweight and/or physically inactive) differ according to an individual's underlying genetic risk of diabetes. It is important to investigate this question to inform strategies for prevention. If, for example, obese individuals with a high level of genetic risk have a higher risk of developing diabetes than obese individuals with a low level of genetic risk, then preventative strategies that target lifestyle interventions to obese individuals with a high genetic risk would be more effective than strategies that target all obese individuals. In this case-cohort study, researchers from the InterAct consortium quantify the combined effects of genetic and lifestyle factors on the risk of type 2 diabetes. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease with those who remain disease free.
What Did the Researchers Do and Find?
The InterAct study involves 12,403 middle-aged individuals who developed type 2 diabetes after enrollment (incident cases) into the European Prospective Investigation into Cancer and Nutrition (EPIC) and a sub-cohort of 16,154 EPIC participants. The researchers calculated a genetic type 2 diabetes risk score for most of these individuals by determining which of 49 gene variants associated with type 2 diabetes each person carried, and collected baseline information about exposure to lifestyle risk factors for type 2 diabetes. They then used various statistical approaches to examine the combined effects of the genetic risk score and lifestyle factors on diabetes development. The effect of the genetic score was greater in younger individuals than in older individuals and greater in leaner participants than in participants with larger amounts of body fat. The absolute risk of type 2 diabetes, expressed as the ten-year cumulative incidence of type 2 diabetes (the percentage of participants who developed diabetes over a ten-year period) increased with increasing genetic score in normal weight individuals from 0.25% in people with the lowest genetic risk scores to 0.89% in those with the highest scores; in obese people, the ten-year cumulative incidence rose from 4.22% to 7.99% with increasing genetic risk score.
What Do These Findings Mean?
These findings show that in this middle-aged cohort, the relative association with type 2 diabetes of a genetic risk score comprised of a large number of gene variants is greatest in individuals who are younger and leaner at baseline. This finding may in part reflect the methods used to originally identify gene variants associated with type 2 diabetes, and future investigations that include other genetic variants, other lifestyle factors, and individuals living in other settings should be undertaken to confirm this finding. Importantly, however, this study shows that young, lean individuals with a high genetic risk score have a low absolute risk of developing type 2 diabetes. Thus, this sub-group of individuals is not a logical target for preventative interventions. Rather, suggest the researchers, the high absolute risk of type 2 diabetes associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001647.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including detailed information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
The Genetic Landscape of Diabetes is published by the US National Center for Biotechnology Information
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
doi:10.1371/journal.pmed.1001647
PMCID: PMC4028183  PMID: 24845081

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